Health care in today’s society revolves around the hospital, but what society truly needs is a transformation away from this reactive approach to a more proactive methodology. Such proactivity entails the development, introduction and implementation of preventive, evidence-based, person-centred care instead of retrospective medical treatments1. In order to facilitate this shift from a focus on disease to well-being, an unmet need exists for the development of transformative technologies. The transformation of health care will require low-cost, compact, mass-producible, disposable and user-friendly devices to enable ever-present continuous health monitoring for mass populations of people at the point of need. Pioneering preventive innovations will enable the capability of facilitating early detection of diseases and timely intervention, decreasing the cost of care (a short-term consequence), improving society’s well-being with an increasingly economically active population (long-term socioeconomic consequences) and taking on ever-present medical challenges (such as late diagnosis and unnecessary medical tests that result in high therapy costs).

The costs of health care are continuing to grow rapidly. In the USA alone, health-care costs were $3.8 trillion in 2019, more than $11,582 per capita, and continue to increase every year at a rate of ~5.4%; they are projected to reach $6.2 trillion in 2028 (Fig. 1a)2,3. In 2019, 17.7% of the gross domestic product for the USA went directly to health-care expenditure, which is estimated to rise to 19.7% of the gross domestic product in 2028 (refs2,3). Health-care expenditures in the USA include private and federal sources, reflecting the two major sectors of the health insurance industry: private insurance companies and federal government aid. Federal aid is further divided into various programmes, including Medicare for people over the age of 65 years, Medicaid for people with a low income, and the Children’s Health Insurance Program for low-income families with children4. Additionally, another portion of the population has neither private nor government insurance; these people avoid receiving treatment until they are in dire need, at which point care is often more costly, and in the worst cases, it is too late for reasonable care (Fig. 1b,c). The UK and many European countries offer more accessible health care than the USA at a greatly reduced cost to the public via government subsidies, but health-care expenditures in these countries have also been consistently increasing. For instance, the UK devoted £225.2 billion to health spending in 2019 (which had almost tripled compared with £78.9 billion in 2000). Thus, the development of a low-cost, home-centred health monitoring platform can contribute to the overall well-being of society and reduce the use of unnecessary clinical expenditures (such as clinical tests) (Box 1).

Fig. 1: Health-care spending and insurance coverage in the world and the USA.
figure 1

a | Total health-care expenditure per capita per country154. b | Percentage of individuals in the USA under the age of 65 years without health insurance coverage, by age group and sex (2015)155. c | Share of health spending based on the country’s income. People in high-income countries have to spend less money out of their pocket, but people in low-income countries cover a considerable portion of their health-care expenses from their own pocket. The low individual income in low-income countries means that people might choose to postpone or avoid routine health-care tests as a result of the assumed cost, waiting until their illness is severe and requires advanced medical care to seek treatment. Consequences of such delays can include missed opportunities for effective therapies154. Parts a and c reprinted with permission from ref.154, World Health Organization. Part b reprinted with permission from ref.155, US Department of Health and Human Services, CDC.

Continuous monitoring enables frequent collection of biomolecular data. Sectors such as wearable technology and funding agencies could encourage researchers to develop novel technologies that collect frequent or continuous data, which could eventually enable the transformation of health care from reactive and hospital-centred to proactive, person-centred, preventive and cost-efficient. Research demonstrating the harmful effects of overscreening of some patients (such as overscreening of patients with prostate cancer) must be considered5,6. Issues related to overscreening in current health-care systems might be because implementation of medical diagnostic tests, in general, is expensive and these single results collected at large time intervals (for example once every few years) are perceived as offering information more valuable than they actually offer and, therefore, a single false-positive result can lead to an immediate medical action and harmful extra treatment; additionally, overscreening leads to unnecessary stress for the healthy person receiving the test. By contrast, seamlessly integrated home-based health-monitoring devices would not cause the stress (as they would be part of people’s daily routines) and offer frequent data collection, which would greatly reduce the importance of individual test results (for example, in some cases, false-positive results) and instead offer statistically significant results, trends and deviations. Thus, the overscreening issue is an outcome of current reactive expensive health-care systems, and this issue can be addressed with research into how to collect more data and interpret data, rather than avoiding collecting data.

The intersection of preventive medicine, scientific innovation, and next-generation technologies that are efficient, effective and affordable could be the key to controlling the ballooning costs of health care. The cost of ‘missed prevention opportunities’ is estimated to be $55 billion every year for the USA7. Research into the development and implementation of low-cost preventive care methods and continuous health-monitoring approaches promises to substantially reduce health-care costs. For instance, vaccination as a preventive measure diminished the annual death toll from smallpox from 48,164 cases, in the twentieth century (typical average during the 3 years before vaccine licensure), to zero, in 2000 (ref.7). Similarly, the annual mortality from measles and rubella decreased from 503,282 and 47,745 cases to 81 and 152 cases in 2000 respectively7,8. Moreover, regular health screening and early detection of diseases can improve the success of treatments and care costs. For example, survival 5 years post-diagnosis in ovarian cancer, which is the fifth most fatal cancer in women9, is 30–40% if it is diagnosed at stage III, whereas survival would drop to <10% if it is diagnosed at stage IV, showing the importance of the early detection of diseases.

Technologies for health-care monitoring using the analysis of blood, saliva, sweat and urine exist. Blood tests can provide information regarding the function of organs (such as the kidneys, liver, heart and thyroid) and also enable diagnosis of diseases such as cancer, diabetes (abnormal level of glucose), clotting, coronary and/or heart diseases, infections, HIV, immune system disorders, bone or blood marrow disorders (abnormal level of haematocrit), thrombotic and/or bleeding disorders (abnormal level of platelets), and thalassaemia (abnormal level of haemoglobin) or sickle cell anaemia10. Despite the indisputable potential of the blood tests, continuous health monitoring with blood requires an invasive collection of blood on a routine basis, engendering discomfort and trypanophobia in patients11. Moreover, a prerequisite for a number of blood tests is fasting (8–12 h before the test), imposing another challenge for continuous blood-based health monitoring.

Analysis of human saliva enables drug usage screening, post-treatment monitoring of therapeutic results, disease diagnosis (such as oral cancer, viral diseases, HIV, and cardiovascular disease) and hormone measurements12,13,14,15. The volume of saliva produced by the human body (500–1,500 cm3 of saliva per day) is enough for performing health monitoring tests; however, high water content (almost 98% of saliva is water) results in a minimal concentration of some of the components (in the range of pg/cm3), making measurements using simple, low-cost equipment complicated13,14.

Sweat is constantly being produced by the body and is accessible non-invasively; thus, this biofluid is a natural candidate for continuous health monitoring. The importance of sweat as an analytical biofluid stems from the sweat formation process. Sweat carries important biomarkers directly derived from blood owing to the concentration gradient between sweat and plasma (based on the Henderson–Hasselbalch equation16 and pH partition theory17, owing to the acidic nature of sweat (pH~6.3) compared with blood (pH~7.4)), causing passive diffusion of plasma components through the lipid bilayer to sweat. Sweat-based monitoring devices come in the forms of a wearable band18, adhesive patches19 and modular devices20. However, the time-consuming sampling process, skin irritation or allergenic reactions of patients caused by the long-term attachment of sensors to the skin, and the small volume of perspired sweat per unit time or area of skin (300–700 cm3 from the entire body daily) are the main limitations of sweat analysis21. Sweat production is also inconsistent (depending on the body activity, temperature and humidity) and the newly precipitated sweat could mix with older sweat on the skin, leading to inaccurate real-time readouts. Interest in sweat analysis is growing with respect to health monitoring (Fig. 2). and it seems that sweat is the preeminent bodily fluid for health monitoring; however, among urine, saliva, sweat, or blood, urine is possibly the best candidate for continuous health monitoring of the future. Thus, embedding urinalysis equipment into toilets offers continuous health-monitoring capability with no expected behavioural change from users, as toilets are ubiquitous and everyone uses toilets regularly, regardless of whether the toilet is equipped with urinalysis apparatus or not.

Fig. 2: Wearable health monitoring publications highlighting sweat-based analysis.
figure 2

Statistics of publications on the topics of wearable and sweat health monitoring, from PubMed. ‘Wearable health monitoring’ represents keyword searches for “wearable health” and “wearable health monitor”; sweat health monitoring’ represents keyword searches for “sweat analysis” and “sweat health monitor”. Publications show an increasing trend in toward wearable health monitoring, specifically sweat-based analysis, for health monitoring.

This Perspective will briefly detail the biological properties of urine and discuss the case for urine being the best candidate for continuous health monitoring in the future. Currently available smart toilets are reviewed, and the development and implementation of devices and methods that will shift the focus of medicine to the well-being of humans, while improving human health and reducing the costs of health care, are discussed22.

Urine — ideal for continuous health monitoring

Urine is defined as a “waste material that is secreted by the kidney…rich in endproduct of protein metabolism together with salts and pigments”23. Within the kidney, nephrons produce urine24. Urine is directly derived from the blood through glomerular filtration in the kidney; therefore, urine contains more of the biological information found in blood than sweat from different organs25,26,27. Urine analysis, or urinalysis, is considered to be one of the oldest medical tests and dates back 6,000 years to Sumerian and Babylonian civilizations28. Urinalysis tests can be broken down into three categories: physical, chemical and microscopic24. Physical properties that can be tested include colour, specific gravity, volume, appearance, and odour. Chemical properties that are testable include pH, sugar, hormones, proteins and peptides (termed proteomics), ascorbic acid, nitrites, urobilinogen, ketones, bilirubin, creatinine, drugs and blood content29,30,31,32. Microscopic analysis includes screening for bacteria, microbiota and the microbiome, yeasts, epithelial cells and crystals24,29,33,34,35. The simplest analysis method is urine test strips, also known as dipsticks. These cost-effective, paper-based multiparameter assays include a number of chemical pads that change colour when they are in contact with urine within a short time (1–2 min)36,37. Despite the simplicity of this method, important properties of urine can be analysed, such as pH, specific gravity, infections, leucocytes, proteins, nitrites, urobilinogen, bilirubin, glucose, ketones and haemoglobin. Urine strip readouts enable clinicians to rapidly detect abnormalities and decide whether further medical tests are needed. However, inaccuracies are conceivable in dipstick-based urinalysis; for example, leakage of reagents between adjacent pads could occur as a result of remaining excess urine on the test strip after being soaked in the urine, causing a mixture of colours and inaccurate readouts. In addition, a number of constituents of urine (such as leukocytes and erythrocytes) tend to sediment at the bottom of the container, increasing the possibility of being inadvertently missed if the urine sample is not mixed properly36. More complex urine tests than dipsticks are also available, such as urine culture38,39,40, urine particle flow cytometers41,42,43, urine cytology44,45,46, microscopy-47,48, mass spectrometry-27 and Raman spectroscopy-based urinalysis49,50. However, these urine tests are not suitable for continuous health monitoring at the point of care (PoC) owing to the requirement for laboratory equipment and expert clinicians to perform the test.

Metabolic phenotypes observed using urinalysis are highly personalized and quantitative as human urine carries >4,500 documented metabolites relating to almost 600 human conditions51. Urine includes potassium, phosphate, sulfate, urea, amino acids, sodium, chloride and important biomarkers for the diagnosis of diabetes, liver diseases, kidney and urinary tract diseases (UTDs), infectious diseases (detected using the presence of nitrites and leukocyte esterase in urine), proteinuria (the presence of protein in urine), and haematuria (the presence of red blood cells in urine)26,52,53,54. In addition to common clinical diagnostic tests (for diabetes and infection) and the detection of pregnancy, urine can be used for more investigational tests, such as diagnosis of breast cancer (the most frequently diagnosed cancer among women), bladder cancer (the most common malignancy of the urinary tract) and prostate cancer (the most frequently diagnosed cancer among men)26,55,56,57,58,59.

In addition to the many biomarkers found in urine, further advantages make urine a suitable candidate for non-invasive continuous health monitoring. First, urine is available in large quantities (400 to 2,000 cm3 per day) and can be non-invasively sampled26. Furthermore, from an analytical perspective, most of the urinary peptides and proteins are relatively small in size (<20 kDa)60 and have high thermodynamic stability60. Moreover, urine has lower sample complexity (that is, less intermolecular interaction) than blood and less complex sample pre-treatment is required owing to the lower protein content than blood60. However, the main challenge of using urine as an analyte is the large natural variation in the concentration of metabolites, proteins and peptides61, necessitating meticulous normalization in measurements61. In addition, the most appropriate time for analysis of urine specimen is within 2 h of collection62, highlighting the importance of urine analysis at the point of collection.

Reported sensitivity for urinalysis varies in the literature, ranging from ~50% (for leukocyte esterase analysis) to ~80% (for dipstick-based protein analysis) to ~100% (for dipstick-based glucose monitoring)52,63. Repeated blood sampling for continuous health monitoring can cause inconvenience to patients owing to the invasive nature of the blood sampling process. Reportedly, 5 out of 10 adults and 7 out of 10 children are needle-phobic64. Moreover, ~25% (~51% of paediatric patients) of first needle insertion attempts fail to access peripheral veins, which can cause bruising at the site of puncture, pain, nerve damage and haematoma65,66,67,68. Thus, urinalysis is a better candidate than blood sampling for continuous health monitoring applications. The accuracy of sweat analysis can reach 98% (for example, for cystic fibrosis analysis)69; however, wearable sweat sensors can cause skin irritation in users70. Furthermore, according to a survey conducted in the USA in 2014, one-third of participating users of wearable activity trackers used wearable devices regularly for no more than 6 months before discontinuing use71. This observation highlights the importance of developing fully automated continuous health monitoring technologies without expecting behavioural changes from the user72. Thus, smart toilet-based urinalysis might be preferred over sweat analysis as it can be easily integrated into people’s daily life.

Thus, considering the potency and limitations of each of the possible bodily fluids (blood, saliva, sweat and urine), urine is the best candidate for continuous health monitoring at the PoC as a wide range of analytes can be detected for early diagnosis of diseases, and the urine is available in large quantities non-invasively.

Toilet-based health monitoring platforms

Urinalysis in the bathroom is advantageous: first, the best timing for urine tests is within 2 h of collection, otherwise refrigeration is needed to avoid bacterial or microbial proliferation in the urine sample, which can cause inaccurate test results62. Furthermore, nowadays, toilets are available almost everywhere (including houses, public places and workplaces), facilitating continuous health monitoring and early detection of diseases. According to the Centers for Disease Control and Prevention, as many as 9 in 10 adults with chronic kidney disease (CKD) do not know they have CKD73, highlighting the vital need for the development of readily available continuous health monitoring platforms to inform patients and caregivers regarding health issues in early stages before complications occur74. Statistically, urinary tract cancers are among the ten most common types of cancer (prostate cancer: ~1,400,000 cases and 375,000 deaths, bladder cancer: ~573,000 cases and 212,500 deaths, and kidney cancer: ~431,200 cases and 179,300 deaths worldwide in 2020)75. Moreover, the economic burden of CKDs was roughly $81.8 billion in the USA in 2018, including diagnosis, drug treatment, kidney transplantation and dialysis76. As smart toilets are able to diagnose a range of health conditions, particularly UTD, they can have a pivotal role in the monitoring and early detection of UTDs, demonstrating an unprecedented opportunity to alleviate mortality, disabilities and economic burden.

The term smart toilet refers to the integration of novel technology with conventional toilets77. One category of smart toilet is toilets equipped with automatic opening and closing lids, water-conserving flushing systems, and autonomous cleaning technologies78,79,80 using different sensors (such as an odour sensor, infrared sensor, sonic sensor, and radio-frequency identification sensor), but no health-monitoring capabilities are proposed78,79,80. Another type of smart toilet can measure health parameters77; in this Perspective the term smart toilet refers to the latter category.

In the 1990s, some of the first patents were filed for smart toilets with the ability to measure rudimentary information (such as urine temperature and body fat81,82,83). However, high cost (for example US$6,100 per unit for the version made in 2008)77, lack of high-value clinical information, requirement for human intervention to record results, and incompatibility with user’s electronic health record (EHR) hampered translation of these early smart toilets into a clinic–home interface77. Since then, attempts have been made to produce cost-effective and practical smart toilets. In this regard, a prototype of a primitive smart toilet was developed for emergency settlements (such as areas hit by natural disasters or refugee camps, in which people are highly susceptible to infections and displacement-associated diseases such as diarrhoea)84 that was able to collect data and distinguish between defaecation, urination and the gender of users for future analysis. In a case study, this setup successfully identified that 60% of visits were for urination and 40% for defecation84. In another study, metal oxide gas sensors (e-nose) were integrated into a toilet to detect the changes in the urine odour85. The fitting of multidimensional data demonstrated more than 95% of the total variance in all the three experiments (evaluated by principal component analysis), suggesting that the e-nose has great potential to separate and discriminate the urine odours in terms of diabetic, healthy and alcohol content85. Another toilet was equipped with a camera that acted as a colour sensor to detect diabetes by taking images of urine (the smart toilet was installed with the camera and was designed to take an image to produce a red–green–blue colour model for image analysis purposes), with ~95% urine colour detection accuracy86. In another experiment, a sensor-embedded toilet seat and a hand-held electrode device were used to measure electrocardiography, body weight, body fat and body bioelectrical impedance87. The body weight and bioelectrical impedance were measured with the accuracy of ±0.128 kg and ±1.933 Ω, respectively. The data obtained were useful for health monitoring, but a number of users might be unwilling to use the hand-held electrode as it is not a routine procedure in the toilet. Furthermore, as the sensors were mounted on the toilet seat, the system was able to record data only in the case of seated urination; considering that a number of men tend to stand to urinate, requiring them to urinate seated would necessitate a behavioural change87.

Startup companies are emerging with innovative smart toilets. Toilet-attachable equipment was devised that can detect blood in human excreta, enabling early detection of colorectal cancer88. This device can be attached to already-in-use toilet bowls, which decreases the overall cost of transferring from conventional toilets to smart toilets88. Another smart toilet was designed to track bowel movements and urination with the aim of detecting dehydration, urinary tract infections and pancreatic disorders89. In addition, integrating analytical devices in standing toilets is of interest. One particular application could be urinal tests in nightclubs to detect the alcohol level of attendees and could be mostly supported by anti-drink–drive campaigns90. Most of the available smart toilets suffer from either limited detectable analytes, or manual steps; most early smart toilet platforms were able to detect only basic health information (such as urine colour and temperature), manual replacement of test equipment (such as dipsticks) inside the toilet bowl might be off-putting for a number of users91. Hence, smart toilets with broader detection abilities that do not require any manual steps or behavioural changes from users are preferable to those that are currently available92,93,94.

A fully automated smart toilet has been developed to measure specific gravity, pH, bilirubin, leukocytes, nitrite, protein, urobilinogen, glucose, erythrocytes, and ketones in urine using a ten-parameter urinalysis strip (Fig. 3a)77. In addition to a test strip, three cameras are used for computer-aided video analysis of the urine stream and image assessment of stool using a machine-learning algorithm. A passive IR motion sensor is installed in order to automatically start data acquisition at the beginning of urination. The setup also possesses a fingerprint module to identify the user and record personal data in a cloud-based health portal wirelessly. Following the completion of urination and analysis, the used strip is automatically discarded into the toilet bowl and is replaced with a new strip, eliminating manual steps in the entire test. The volume measurement results of machine learning-based and standard uroflowmetry were comparable with 0.92 linear correlation (which measures the strength of the linear relationship between two variables77). Thus, the machine-leaning algorithm was able to accurately measure urine volume, similar to the conventional standard uroflowmetry method77.

Fig. 3: Toilet-based continuous health monitoring platforms.
figure 3

a | Toilet-based analysis of urine and stool. Using motion sensors, a ten-parameter urinalysis strip and three cameras, this smart toilet is able to automatically detect the beginning of urination and defaecation to start data acquisition and analysis of different biomarkers, urine stream and stool images. A fingerprint scanner identifies the user in order to save acquired data to a cloud-based health portal77. bd | A 3D-printed sample collector for toilet-based urinalysis. b | The 3D-printed sample collector is attached to the toilet bowl wall to collect and direct the urine to a urine analyser. c | Exploded view of the proposed setup. d | The urinalysis capability of the platform was tested by quantifying protein concentration in prepared samples, showing acceptable performance and accuracy of toilet-based urinalysis91. Part a reprinted from ref.77, Springer Nature Limited. Parts bd reprinted from ref.91, CC BY 4.0 (

A platform was developed to test prototypes of urine sample collectors, enabling automated sample handling, collection, quantification and flushing capabilities, and a novel 3D-printed sample collector design for long-term toilet-based urine analysis was proposed and evaluated using the prototype testing platform91. In addition to a water pump and a stepper motor, the setup also comprised a stereolithography-3D-printed sample collector with an optimized shape and drain angle placed in the toilet bowl wall, which collects and directs urine to a toilet-based urine analysis module (Fig. 3bd). In order to validate the analytical performance of the platform, the collected samples were tested for protein concentration using commercially available dipsticks and plate readers and showed agreement between the results. Protein concentration as low as 0.1 g/l was detected consistently with an average standard deviation of ± 0.018 g/l after repeating the measurements for 100 cycles (urination–flushing–urination). Last, as high surface roughness of the sample collector was speculated to retain a small portion of urine sample constituents (such as protein), causing inaccurate readouts, the possible effects of the employed 3D printing fabrication approach on the sample collecting efficiency were examined, reporting no meaningful discrepancy between stereolithography-based, inkjet-based, or fused-deposition-based 3D printed sample collectors. The urinalysis with this setup required no behavioural change from the user or manual steps as the collector was cleaned automatically at the end of sample collection by flushing, ensuring a hygienic platform and avoiding sample mixture in future tests91.

A voiding diary (that is, a daily record of a patient’s bladder activity) is a common non-invasive method for collecting real-time information regarding urinary dysfunction symptoms (such as symptoms of frequency, urgency and urinary incontinence episodes), which can be used as a diagnostic method or an approach to monitoring the effectiveness of the adopted therapy95,96. However, in this method, patients should strictly follow instructions to collect urine samples manually, measure the volume and record data for at least 24 h (or for 3 or 7 days in some circumstances), causing reluctance in a number of patients to monitor their health status96. However, potential smart toilets could automatically identify the user, use sensors to detect the beginning of the urination, measure and record the volume of excreta, record the date and time of each urination, and share the results with the caregiver remotely.

Smart toilets are an emerging field of study, meaning that the majority of developed smart toilets are proof-of-concept platforms rather than commercial platforms with thorough test capabilities for disease diagnosis. To date, smart toilets have integrated cameras and/or sensors (for physical inspection) and dipsticks (for chemical tests)77,91. Hence, smart toilets can detect diseases that can be diagnosed using physical urine tests (such as volume, flow consistency, colour, and odour) or dipstick tests (such as kidney or urinary tract disorders (pH and protein level abnormality), dehydration (specific gravity abnormality), diabetes (sugar (glucose) or abnormal ketone levels) and liver disorder (abnormal levels of bilirubin))36,37,52,97. Further integration of increasingly complex urinalysis (such as urine culture, urine particle flow cytometers, urine cytology, microscopy-, mass spectrometry- and Raman spectroscopy-based urinalysis) into smart toilets could improve the analytical capabilities of smart toilets in the future.

In addition to urinalysis, smart toilets can be equipped to have a role in women’s health management. Abnormal menstrual patterns (such as excessive menstrual blood loss) can be attributed to gynaecological diseases such as genital tuberculosis, infertility, sexually transmitted diseases, ovarian cancer, polycystic ovarian disease, cervical cancer, or uterine fibroid, adversely affecting a woman’s social, physical and emotional life98,99. In addition, the colour of the lost menstrual blood can be correlated with health conditions such as low oestrogen (pink), polyps or fibroids (bright red), lochia (dark red) and infection (grey or orange)100,101. Early identification of abnormalities (particularly in adolescence) can prevent possible complications in adulthood. Smart toilets could be used as women’s health management platforms by collecting vaginal discharge, recording menstrual patterns and period blood colour analysis.

Another potential application of smart toilets is stool analysis. Human stool contains water, protein, undigested fats, polysaccharides, bacterial biomass, ash and undigested food residues102 that provide useful biological information for the diagnosis of gastrointestinal disorders, including cancer (such as colorectal cancer), pancreatitis, poor nutrient absorption and infection (such as bacterial, viral or parasitic infections)72,77. A comprehensive stool analysis might include microscopic examination and immunological, chemical and microbiological tests, which require manual faeces collection and clinicians to perform the tests. Smart toilets can be integrated with further equipment to collect health data from defaecation to provide a more comprehensive perspective of user’s health than urine alone can provide. Smart toilets can prevent unnecessary laboratory investigations by continuously and automatically tracking basic parameters in a stool sample (such as colour, odour, shape, amount, pH, consistency and the presence of mucus) to detect abnormalities and inform the user and/or caregivers whether further clinical tests are required. As one of the first attempts at toilet-based stool analysis, stool images (taken by a camera) from the toilet bowl and the defecation time (obtained using a pressure senor below the toilet seat) were analysed using a convolutional neural network (CNN)77. In this platform, the final layer of a previously trained Inception v.3 deep CNN model was retrained with new categories—toilet states and stool states. Stool classification accuracy of the developed CNN was 85.45%, determined using expert-annotated stool images according to the Bristol Stool Form Scale77 A bidirectional relationship between the enteric and the central nervous systems has been established, linking cognitive and/or behavioural and emotional centres of the brain with peripheral intestinal functions of gut microbiota (also known as the microbiota–gut–brain axis)103. The gut–brain axis correlates with stress, anxiety, depressive-like behaviours, schizophrenia, Parkinson disease, Alzheimer disease, obesity and autism104, demonstrating the potential of stool analysis, which is achievable using cameras, sensors and test strips in smart toilets, to aid monitoring of mental health.

Rather than performing all complex available tests in the toilet, the goal of smart toilets is to continuously monitor important biomarkers, rapidly and inexpensively, in order to inform users about their health status and assist clinicians to decide, in the case of abnormalities, whether complex follow-up tests are needed or not. Smart toilets are supposed to integrate into people’s daily life and ultimately replace a number of wearable health monitoring devices (such as wearable sweat analysis devices, as they can cause skin irritation), smart toilets can occupy a share of the wearable device market, which is estimated to reach $73 billion by 2022 (ref.105). From an economic perspective, the smart bathroom market size (smart toilet, soap dispenser, tap and shower) was ~$3 billion in 2019 in the USA and is projected to grow at a compound annual growth rate of 10.5% between 2020 and 2027 (ref.106). Smart toilets (self-cleaning and self-deodorizing toilets) constitute 33.6% of the smart bathroom market revenue, which can be considered as an incentive for bathroom accessory manufacturers to invest in the development of health monitoring smart toilets106. In 2020, bathrooms were the most frequently remodelled rooms in houses in the USA106. Also, the cost of smart toilets is expected to decrease as a result of the emergence of startup companies. Thus, considering the health monitoring benefits offered by smart toilets, the possibility that people will be inclined to replace their current toilets with smart toilets when remodelling their bathrooms is feasible.

Challenges and future perspectives

The further development and success of toilet-based continuous health-monitoring platforms are subject to overcoming current challenges, adopting current technologies from other scientific fields and compliance with future innovations. Using emerging technologies such as machine-learning algorithms, cloud-based data storage and the internet of things (IoT), along with microfluidic chips, has the highest potential contribution to the future of toilet-based health-monitoring platforms107,108,109,110. Furthermore, considering user expectations, addressing current privacy concerns, and using environmentally friendly substances can result in increased user acceptance, which is key for any novel device to thrive.

Internet of Things, machine learning and cloud-based storage

Effective sample collection and accurate data acquisition and analysis have been the focus of most of the smart toilets that have been developed to date; however, long-term data storage, classification, analysis and interpretation of results are also important. Currently developed smart toilets are able to quantify and analyse data autonomously, but the ultimate goal is to share obtained data with the user (via a personal computer or smartphone) and caregivers (such as hospital servers) in real time. Thus, a suitable data transfer technology should be proposed. For short-range wireless communications, Bluetooth low energy is a suitable choice as it is secure and possesses a good range (150 m) with low latency and low power consumption36,111. For long-range wireless communications, SigFox, long-range (LoRa) and narrowband IoT (NB-IoT) standards can be used36. SigFox is a low-power, long-range (10 km (urban) and 40 km (rural)), low-data rate form of the wireless connection112. NB-IoT is a secure long-range (15 km) standard with a high-network capacity, data transfer rate and band of operation, which is already used in health-care applications113. However, this standard is licensed and has a relatively complex structure to implement, hindering its widespread application36,114. LoRa is a bidirectional, non-licensed, cost-effective, low-data-rate, and easy-to-access band (5 km (urban) and 15 km (rural)) that can still satisfy the requirements of smart toilets115,116. In the future, assuming ubiquitous substitution of smart toilets with conventional toilets, the size of data transferred from a large population to the caregivers and/or clinicians would be copious, complicating storage, management and analysis of available data.

Commencement of cloud-based data storage and computing servers enables preservation and on-demand access to shared data for patients, clinicians or any authorized individual or firm, regardless of the location of the user117. Cloud-based systems are cost-efficient owing to the centralization of infrastructures and division of costs among a large pool of users118. After storing data, the management and analysis of the large amount of stored data would outpace the capacity of human experts. Certain machine-learning algorithms enable computers to learn how to quantify and classify data without being explicitly programmed for that specific task108. Thus, machine-learning methods can save time and money by rapidly classifying a large number of smart toilet users into categories, such as within normal (meaning no action is needed), minor abnormality (meaning preventive measures are needed), moderate abnormality (meaning further health monitoring and tests are needed), and severe abnormality (meaning immediate medical intervention is needed). Accordingly, users can track their health condition while clinicians can focus available medical resources on at-risk groups, increasing the success rate of therapies and minimizing the number of unnecessary costly medical tests. Applying this methodology at the nation level would provide authorities (such as governments) with useful information based on the aggregated health condition of the population for future decision-making7. In addition, cloud-based servers can provide a worldwide EHR database consisting of anonymous medical records annotated by experts, facilitating the development of increasingly precise machine-learning algorithms in the future119. IoT means the use of specific protocols for interdevice and internet communication, enabling real-time access and remote management of devices118.

The integration of IoT, machine learning and cloud-based storage can link all in-home health-monitoring platforms (such as smart toilets, smartwatches, wearable sensors, heart-rate monitors and blood pressure measurement devices), leading to a transformation from hospital-centred health care to in-home seamless health monitoring systems to follow all health aspects of the patient (such as personalized medication, vital sign monitoring and on-site diagnosis) and share important indicators with caregivers120 (Fig. 4).

Fig. 4: Future perspective of continuous health monitoring using smart toilets.
figure 4

Integration of sensors, cameras and analytical assays into toilets can facilitate home-based analysis of human excreta. The obtained data can be transferred to a cloud-based data storage system where machine learning algorithms can be used to classify and quantify data, providing health status information for clinicians and users.

Data privacy

Despite the current popularity of cloud computing and EHRs, outsourcing data to be managed by a third-party organization provokes security and privacy concerns121,122. Health records, in particular, are sensitive personal data. For instance, leaked personal health data might be tempting for insurance companies to use to identify uninsurable people owing to a genetic disorder or medical history123. Thus, security protocols, access control mechanisms and regulations are necessary, yet challenging owing to the complexity of cloud computing platforms124,125,126. Security measures are being developed to keep data confidential from both cloud providers and other cloud users, such as private single-client computing, private multi-client computing, cryptographic and non-cryptographic approaches124,127. In addition, encrypting data before outsourcing (end-to-end encrypted data transfer) is a relatively old yet useful method that can be integrated into smart toilet platforms. However, this approach requires considerable time for caregivers to decrypt the entire available data so that they can search the records123. The blockchain method introduced a promising solution for secure health data management123. Nonetheless, the complexity of the blockchain structure, its relatively high cost, and permanent retention of data on blockchain nodes are challenges to be addressed in the future to facilitate widespread use of blockchain in health-care applications, particularly smart toilets122,123,128,129.

Smart toilets involve access to genitalia, which might cause reluctance in users to use these toilets owing to the untransparent extent and purpose of data acquisition. Informed consents are ethical and legal obligations that have been in use in health-care research through which participants are informed regarding what data will be acquired, the intention of data acquisition, and who can have access to the data130,131. With increased implementation of smart toilets, a similar consent document can be designed for smart toilet users by caregivers, so that users would be informed in advance regarding risks, benefits, test details, can voluntarily choose which test they are willing to take or they can add an expiration date for recorded data.

User acceptance

User acceptance is key for the successful translation of an emerging proof-of-concept design and to increase its effect in society, particularly in the case of smart toilets that are directly related to the privacy of users (that is, genitalia)132. According to the technology acceptance model, user acceptance of new technologies happens in three stages: first, external factors (beliefs of individuals) form perceived usefulness and perceived ease of use in potential users; second, perceived usefulness and perceived ease of use affect the behavioural intention to accept such a system; and third, behavioural intention leads to the actual use of the system133,134. Hence, in addition to engineering considerations, behaviours, beliefs and perceptions of users should be taken into account in the design process. A promising method to increase user acceptance is a user-centred design approach (that is, involving users in the design and modification process of new platforms with interviews, questionnaires and public surveys135,136), which enables designers to satisfy user expectations and address their concerns. For example, a survey conducted at Stanford University asking about the tendency of participants to use smart toilets reported that 30% of participants felt uncomfortable using smart toilets, especially in the case of camera-equipped platforms77. Sonouroflowmetry can be used instead of cameras to measure flow rate and volume of urine without optically scanning the user’s body parts, which might improve user acceptance77. Moreover, current toilet-based urinalysis setups are designed for sitting toilets (known as western-style toilets) and modifying these setups to be compatible with squatting toilets would broaden consumer acceptance to other cultures and countries. Another aspect of user acceptance is convincing users of the importance and usefulness of continuous health monitoring. In other words, as preventive measures reveal their benefits in the long term, except for people suffering from certain diseases (such as diabetes), users may not be motivated enough to perform tests routinely for a long period to experience the benefits133. Thus, clarifying benefits of continuous health monitoring and designing fully automated platforms (with no behavioural change required) can increase the acceptance of smart toilets in society in the future.

Optimal frequency of health monitoring

Cameras and sensors are embedded in smart toilets to monitor specific parameters (for example, flow rate, odour and colour) with no need for the replacement of equipment after each urination. However, a number of tests use analytical devices (such as dipsticks) that need to be substituted automatically after each test, highlighting the importance of determining an optimal frequency for toilet-based health monitoring tests (daily, weekly, biweekly) to avoid unnecessary expense. For instance, heart health monitoring might need to be performed as frequently as multiple times per minute for arrhythmia diagnosis, but cancer detection might require PoC tests every few weeks72. In this regard, genomic screening and analysis of the exposome (external factors (such as infectious agents and pollutants), internal exposures (such as the body’s microbiome and oxidative stress)) and the social determinants of health (for example, neighbourhood density and walkability, education and socioeconomic status)72 can be used to create a personalized disease risk profile to assist caregivers to focus on diseases relevant to an individual, to determine the tests needed for a specific patient, analytes of interest and frequency of tests. Integration of the IoT, cloud-based computing, smart toilets and personalized risk profiles enable caregivers to remotely personalize the type and frequency of tests that a smart toilet should perform after identifying the user. Consequently, the effectiveness and cost efficiency of toilet-based continuous health monitoring will improve while preventing conceivable issues such as alert fatigue. Alert fatigue means ignorance or desensitization to a number of safety alerts owing to the presence of an overwhelming number of alerts received by the user, which can be prevented by increasing the specificity of alerts (lowering the frequency of health monitoring tests), prioritizing or sorting important alerts using machine-learning algorithms and consolidating redundant alerts.

Overall, the smart-bathroom industry and scientific research will pave the way for big data around human biomolecular signatures and personalized risk profiles to identify health trends in patients and prevent further complications with timely medical intervention.

Microfluidics devices

Current proof-of-concept toilet-based platforms are designed to perform the same set of measurements or tests for all users. However, future smart toilets could offer a wide range of patient-specific urine tests, specified by the caregiver based on the risk assessment of the user to have increased sensitivity to certain biomarkers137,138. Microfluidic setups are a promising candidate for PoC pretreatment of collected urine sample (if pretreatment is needed before urinalysis) and analysis of urine139. Microfluidic devices are portable, cost-effective, automated, high-throughput, and multitarget analytical devices that can conduct tests with minimal sample volume, low power consumption, and low chance of contamination140,141,142 with a wide range of applications in biomedical sciences from lab-on-chip141,143,144 to organ-on-chip145,146,147,148 technologies. A future smart toilet could be equipped with different sets of microfluidic chips while connecting to the clinicians through a cloud-based IoT system to load a certain chip in the smart toilet after identifying the user.

Environmentally friendly test strips

Paper-based urinalysis strips have been widely used26,149. As well as being easy to produce, disposable, low-cost and pumpless (meaning no external power source is needed), different reagents can be embedded in paper strips to enable multianalyte measurements136. Moreover, owing to the disposability of paper, smart toilets can automatically discard paper strips to the sewage after being used and replace them with a new strip with no need for manual steps by the user. However, ubiquitous production and use of paper-based devices create environmental concerns, such as deforestation and climate change. Attempts have been made to find more environmentally friendly substitutes than trees to produce paper-based microfluidic analytical devices, such as cotton-based, thread-based and hemp-based devices150,151,152,153. As a first attempt, a hemp-based paper microfluidic strip was fabricated and examined for urinalysis, showing a comparable analytical performance with typical paper153. Future studies could consider further characterization and development of alternatives to typical paper with increased sustainability for the production of paper-based urinalysis strips in order to maintain analytical accuracy, while addressing environmental concerns.


Among the four bodily fluid candidates for use continuous health monitoring — urine, saliva, blood and sweat — urine demonstrates the best candidacy. The invasiveness of blood tests hampers their suitability for continuous health monitoring and saliva samples contain a limited number of important biomarkers. Plenty of analytes are found in sweat and the growing interest in wearable sweat sensors; however, inconsistent production of sweat, the mixture of newly precipitated sweat with old sweat on the skin and skin irritation from the adherence of the device collecting sweat are the main challenges of sweat-based continuous health monitoring platforms.

Several reasons exist for choosing urine analysis over sweat analysis for continuous health monitoring. Urine monitoring is most likely to satisfy the behavioural preferences of users: urine collection is painless, non-invasive (totally free of contact with skin), and does not cause any discomfort. Furthermore, urine collection is also discreet, occurring in the privacy of a restroom, and requires no added burden, as everyone urinates with or without monitoring.

Overall, toilet-based technologies are in their infancy and have a strong potential to seamlessly collect and analyse data frequently, enabling new strategies for early screening. Current smart-toilet technologies have not shown diagnosis of a disease that was not possible by other means; however, embedding urine tests in toilets facilitates convenient, in-home and patient-specific continuous health monitoring. Smart toilets not only can directly inform users regarding their health but they also provide a platform for sharing health data in real time with caregivers for early diagnosis of diseases, and they can aggregate valuable health data from society for policymakers. Ultimately, the integration of smart toilets with machine learning, IoT, and cloud-based servers will result in early detection of diseases, increasingly effective treatments, reduced health-care costs and improved monitoring of health trends in society.