Smart Toilet Research Articles

P0712 Feasibility of a smart toilet seat for the measurement of physiological parameters in active Ulcerative Colitis: A pilot study.

Abstract Background Management of inflammatory bowel disease (IBD) remains largely reactive, with limited ability to predict or prevent flares. Passive remote monitoring of physiological parameters could provide objective data on IBD activity and facilitate the development of algorithms for early flare detection. OnePlanet Research Center has developed the smart toilet seat, a novel device that noninvasively measures several physiological parameters during a toilet visit (Figure 1). In this study, we aimed to 1) evaluate feasibility of using the smart toilet seat for home monitoring for ulcerative colitis (UC) 2) measure physiological parameters in UC patients starting new medical treatment during a flare using the smart toilet seat. Methods We installed the smart toilet seat at the homes of eight patients with active UC starting new treatment for a period of 8 weeks. Patients were instructed to use the smart toilet seat as they would use their regular toilet, and indicate their toilet visit and stool consistency using remote control buttons. Measurements included number of toilet visits, toilet visit duration, stool consistency, heart rate and perianal temperature. Disease activity was measured weekly using an app-based self-reported Simple Clinical Colitis Activity Index (SCCAI) and faecal calprotectin (FC) (CalproSmart Self Test). Results 33 patients were contacted for participation, of which eight patients with active UC were included (24%). Next to maintenance therapy with aminosalicylates, patient started with various types of treatment (Table 1). Median FC was 1107 µg/g (range <77 – >1500 µg/g (lower and upper levels of detection)) at week 1 and below the lower limit of detection (<77 µg/g (<77 – >1500 µg/g)) at week 8. 1431 visits to the Smart toilet seat were recorded, of which 326 were defaecation visits. Median toilet visit duration for all visits and defaecation visits was respectively 1.8 and 4.2 minutes. Between week 1 and week 8, a median change of -6 (range -19, 4) in weekly bowel frequency was measured by the Smart toilet seat and median change in weekly SCCAI was -2 (range -4.5, -0.5) (R2 = 0.75). All participants indicated they would use the Smart toilet seat in the future if it would give their gastroenterologist or general physician more insight into their disease. Conclusion Our study shows it was feasible to measure stool frequency and multiple physiological parameters using a smart toilet seat in a population of UC. Future research is aimed to identify relevant parameters measured by the smart toilet seat that are related to the development of IBD flares, which could serve as basis for predictive models.

Long-term, automated stool monitoring using a novel smart toilet: A feasibility study.

Patients' report of bowel movement consistency is unreliable. We demonstrate the feasibility of long-term automated stool image data collection using a novel Smart Toilet and evaluate a deterministic computer-vision analytic approach to assess stool form according to the Bristol Stool Form Scale (BSFS). Our smart toilet integrates a conventional toilet bowl with an engineered portal to image feces in a predetermined region of the plumbing post-flush. The smart toilet was installed in a workplace bathroom and used by six healthy volunteers. Images were annotated by three experts. A computer vision method based on deep learning segmentation and mathematically defined hand-crafted features was developed to quantify morphological attributes of stool from images. 474 bowel movements images were recorded in total from six subjects over a mean period of 10 months. 3% of images were rated abnormal with stool consistency BSFS 2 and 4% were BSFS 6. Our image analysis algorithm leverages interpretable morphological features and achieves classification of abnormal stool form with 94% accuracy, 81% sensitivity and 95% specificity. Our study supports the feasibility and accuracy of long-term, non-invasive automated stool form monitoring with the novel smart toilet system which can eliminate the patient burden of tracking bowel forms.

IoT-Based Smart Toilet System to Detect Bacteria and Amount of Uric Acid in Urine

IoT-Based Smart Toilet System to Detect Bacteria and Amount of Uric Acid in Urine

Implementation of Smart Toilets as An Innovative Business Model in Public and Private Institutions

Implementation of Smart Toilets as An Innovative Business Model in Public and Private Institutions

Self-Sustaining Triboelectric Nanosensors for Real-Time Urine Analysis in Smart Toilets.

Healthcare has undergone a revolutionary shift with the advent of smart technologies, and smart toilets (STs) are among the innovative inventions offering non-invasive continuous health monitoring. The present technical challenges toward this development include limited sensitivity of integrated sensors, poor stability, slow response and the requirement external energy supply alongside manual sample collection. In this article, triboelectric nanosensor array (TENSA) is introduced featuring electrodes crafted from laser-induced 3D graphene with functional polymers like polystyrene, polyimide, and polycaprolactone for real-time urine analysis while generating 50 volts output via urine droplet-based triboelectrification. Though modulating interfacial double-layer capacitance, these sensors exhibit exceptional sensitivity and selectivity in detecting a broad spectrum of urinary biomarkers, including ions, glucose, and urea with a classification precision of 95% and concentration identification accuracy of up to 0.97 (R2), supported by artificial neural networks. Upon exposure to urine samples containing elevated levels of Na+, K+, and NH4 +, a notable decrease (ranging from 32% to 68%) is observed in output voltages. Conversely, urea induces an increase up to 13%. Experimental validation confirms the stability, robustness, reliability, and reproducibility of TENSA, representing a significant advancement in healthcare technology, offering the potential for improved disease management and prevention strategies.

Examining Emotional Factors of Smart Toilets Design for China's New Elderly

The emergence of a “new elderly” group in China with unique emotional needs has highlighted the importance of smart toilet design. This study explores the emotional needs of the new elderly and the potential benefits of specialized sanitary products in addressing their emotional well-being. The study developed a framework based on Norman’s Three-level theory of Emotional Design (NTTED) and Maslow's Hierarchy of Needs Theory (MHNT) which have been widely used in the field of emotional design. A quantitative method is employed and a questionnaire survey was conducted to collect the data by using cluster sampling from the high-tech zone in Chengdu, Sichuan, China, about 501 respondents participated among 394 are new elderly and 108 are traditional elderly. The findings from a case study involving 501 elderly participants emphasize the significance of emotional factors in product design for the new elderly, specifically in the context of smart toilets in China. High-quality materials, personalized safety features, and cognitive enhancement contribute to user satisfaction. Lastly, the identified pain points underscore the need for privacy and safety measures, clear instructions, and sensory comfort. Enhancements in maneuverability address limited deftness and instruction readability, while adaptability focuses on tactile and visual cues. Respect, belonging, and progression considerations are pivotal in ensuring user satisfaction and well-being. This study identifies the emotional factors in product design for the new elderly in China to enhance emotional design in smart toilets for this demographic.

DEVELOPMENT OF SMART TOILET SYSTEM FOR OLD AGE AND SPECIALLY ABLED PERSON

This research paper presents the development of a novel smart toilet system designed to the unique needs of elderly individuals and persons with disabilities. The primary objective of the project is to monitor the health conditions of users while inside the toilet, this system incorporates advanced sensors, user-friendly interfaces, and automation to provide a seamless and dignified toileting experience for individuals with diverse mobility challenges. Integral to the system is a comprehensive health monitoring component. Utilizing sensors, the system measures the user's body temperature and heartbeat. This real-time health data is crucial for timely detection of potential health issues, promoting preventive healthcare and immediate response if needed. This research contributes to the evolving field of assistive technologies, specifically addressing the needs of aging populations and individuals with disabilities. The integration of mechanical design, health monitoring, and cloud-based connectivity exemplifies a holistic approach to smart healthcare solutions, fostering independence and improved quality of life for users. Key Words: Smart and healthy living, Emergency assistance, Care, Health.

Optimized design of smart toilet spray bar flow channel based on CFD

Currently, there are significant issues with the intelligent toilets available on the market, such as a large swing of the spray bar when spraying water column and eccentricity. This paper presents four optimization schemes for the intelligent toilet spray bar channel by analysing and redesigning a specific model. Computational Fluid Dynamics (CFD) technology was employed to analyse the water flow conditions inside the spray bar of the intelligent toilet. The calculation results indicate that for the same water inlet flow rate, the optimal structure in optimization scheme 1 is the block with a length (L) of 1.5 mm. In optimization scheme 2, the optimal structure is the block located at position (3). In optimization scheme 3, the optimal structure is a 15° rotation, which exhibits the least swirls and pressure loss while maintaining the prototype structure. When the prototype structure is modified, the optimal structure in optimization scheme 4 is the excision bend pipe. This structure exhibits the lowest swirl number and pressure loss among all the optimization schemes. The determined structure offers valuable insights for enhancing the flow characteristics of the improved product.

A hybrid feature selection approach for urinary tract infection detection and prediction in IoT-Fog environment

Urinary Tract Infections (UTIs) are a prevalent health concern experienced by millions of individuals worldwide and have a significant impact on overall well-being. The clinical presentation of UTIs varies depending on their location and severity. Common symptoms include discomfort during urination, increased frequency, a compelling urge to urinate, discomfort in the lower abdomen, and the presence of blood in the urine (hematuria). Severe cases may be accompanied by fever, pain in the flank region, and other systemic symptoms, indicative of an upper UTI. Precise diagnosis of UTIs is achieved through clinical evaluation and laboratory tests. The primary aim of this research is to utilize appropriate Machine Learning (ML)-based algorithms to predict Urinary Tract Infections (UTIs). The ultimate goal is to develop a predictive model that can be effectively implemented in a smart toilet system. By achieving this objective, this research seeks to provide an innovative and pragmatic solution for UTI prediction, harnessing the potential of ML algorithms in IoT-Fog settings to enhance healthcare and promote public health. This paper introduces a hybrid approach that combines feature selection methods and employs the Guided Regularized Random Forest (GRRF) classification algorithm to aid in the diagnosis of UTIs. A UTI dataset was created using data from routine examinations and definitive diagnostic outcomes for UTI patients. Dimensionality reduction was carried out using Principal Component Analysis (PCA), while feature selection was performed using K-best and Lasso CV techniques. Through the proposed strategy, this study achieved a remarkable accuracy rate of 98.8% and a precision rate of 98.90% in UTI identification. Future UTI prevention and treatment plans must be optimized via further research and ongoing efforts to overcome antibiotic resistance.

Older Adults' Perceptions About Using Intelligent Toilet Seats Beyond Traditional Care: Web-Based Interview Survey.

In contemporary society, age tech (age technology) represents a significant advancement in health care aimed at enhancing patient engagement, ensuring sustained independence, and promoting quality of life for older people. One innovative form of age tech is the intelligent toilet seat, which is designed to collect, analyze, and provide insights based on toileting logs and excreta data. Understanding how older people perceive and interact with such technology can offer invaluable insights to researchers, technology developers, and vendors. This study examined older adults' perspectives regarding the use of intelligent toilet seats. Through a qualitative methodology, this research aims to unearth the nuances of older people's opinions, shedding light on their preferences, concerns, and potential barriers to adoption. Data were collected using a web-based interview survey distributed on Amazon Mechanical Turk. The analyzed data set comprised 174 US-based individuals aged ≥65 years who voluntarily participated in this study. The qualitative data were carefully analyzed using NVivo (Lumivero) based on detailed content analysis, ensuring that emerging themes were coded and classified based on the conceptual similarities in the respondents' narratives. The analysis revealed 5 dominant themes encompassing the opinions of aging adults. The perceived benefits and advantages of using the intelligent toilet seat were grouped into 3 primary themes: health-related benefits including the potential for early disease detection, continuous health monitoring, and seamless connection to health care insights. Technology-related advantages include the noninvasive nature of smart toilet seats and leveraging unique and innovative data collection and analysis technology. Use-related benefits include ease of use, potential for multiple users, and cost reduction owing to the reduced need for frequent clinical visits. Conversely, the concerns and perceived risks were classified into 2 significant themes: psychological concerns, which included concerns about embarrassment and aging-related stereotypes, and the potential emotional impact of constant health monitoring. Technical performance risks include concerns centered on privacy and security, device reliability, data accuracy, potential malfunctions, and the implications of false positives or negatives. The decision of older adults to incorporate intelligent toilet seats into their daily lives depends on myriad factors. Although the potential health and technological benefits are evident, valid concerns that need to be addressed remain. To foster widespread adoption, it is imperative to enhance the advantages while simultaneously addressing and mitigating the identified risks. This balanced approach will pave the way for a more holistic integration of smart health care devices into the routines of the older population, ensuring that they reap the full benefits of age tech advancements.

From-Toilet-to-Freezer: A Review on Requirements for an Automatic Protocol to Collect and Store Human Fecal Samples for Research Purposes.

The composition, viability and metabolic functionality of intestinal microbiota play an important role in human health and disease. Studies on intestinal microbiota are often based on fecal samples, because these can be sampled in a non-invasive way, although procedures for sampling, processing and storage vary. This review presents factors to consider when developing an automated protocol for sampling, processing and storing fecal samples: donor inclusion criteria, urine-feces separation in smart toilets, homogenization, aliquoting, usage or type of buffer to dissolve and store fecal material, temperature and time for processing and storage and quality control. The lack of standardization and low-throughput of state-of-the-art fecal collection procedures promote a more automated protocol. Based on this review, an automated protocol is proposed. Fecal samples should be collected and immediately processed under anaerobic conditions at either room temperature (RT) for a maximum of 4 h or at 4 °C for no more than 24 h. Upon homogenization, preferably in the absence of added solvent to allow addition of a buffer of choice at a later stage, aliquots obtained should be stored at either -20 °C for up to a few months or -80 °C for a longer period-up to 2 years. Protocols for quality control should characterize microbial composition and viability as well as metabolic functionality.

Empowering elderly care with intelligent IoT-Driven smart toilets for home-based infectious health monitoring

The COVID-19 pandemic highlights the need for effective and non-intrusive methods to monitor the well-being of elderly individuals in their homes, especially for early detection of potential viral infections. Conspicuously, the present paper develops a Multi-scaled Long Short Term Memory (Ms-LSTM) model for the routine health monitoring of elderly patients to detect COVID-19. The proposed method offers home-based health diagnostics through urine analysis by leveraging the IoT-Fog-Cloud paradigm. Mainly, the proposed model constitutes a four-layered architecture: data acquisition, fog layer, cloud layer, and interface layer. Each layer serves distinct functionalities and provides specific services, thereby collectively enhancing the overall effectiveness of the model. The statistical results of the study demonstrate the superior performance of the proposed Ms-LSTM model in comparison to state-of-the-art methods, including Artificial Neural Networks (ANN), K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Random Forest, and LSTM. Further, the proposed model attains a mean temporal efficiency of 39.23 seconds. It exhibits high reliability (92.97%), stability (70.06%), and predictive accuracy (93.25%).

Perceived Use Cases, Barriers, and Requirements for a Smart Health-Tracking Toilet Seat: Qualitative Focus Group Study.

Smart bathroom technology offers unrivaled opportunities for the automated measurement of a range of biomarkers and other data. Unfortunately, efforts in this area are mostly driven by a technology push rather than market pull approach, which decreases the chances of successful adoption. As yet, little is known about the use cases, barriers, and desires that potential users of smart bathrooms perceive. This study aimed to investigate how participants from the general population experience using a smart sensor-equipped toilet seat installed in their home. The study contributes to answering the following questions: What use cases do citizens see for this innovation? and What are the limitations and barriers to its everyday use that they see, including concerns regarding privacy, the lack of fit with everyday practices, and unmet expectations for user experience? Overall, 31 participants from 30 households participated in a study consisting of 3 (partially overlapping) stages: sensitizing, in which participants filled out questionnaires to trigger their thoughts about smart bathroom use and personal health; provotyping, in which participants received a gentle provocation in the form of a smart toilet seat, which they used for 2 weeks; and discussion, in which participants took part in a web-based focus group session to discuss their experiences. Participants mostly found the everyday use of the toilet, including installation and dismantling when necessary, to be relatively easy and free of complications. Where complications occurred, participants mentioned issues related to the design of the prototype, technology, or mismatches with normal practices in using toilets and hygiene. A broad range of use cases were mentioned, ranging from signaling potentially detrimental health conditions or exacerbations of existing conditions to documenting physical data to measuring biomarkers to inform a diagnosis and behavioral change. Participants differed greatly in whether they let others use, or even know about, the seat. Ownership and control over their own data were essential for most participants. This study showed that participants felt that a smart toilet seat could be acceptable and effective, as long as it fits everyday practices concerning toilet use and hygiene. The range of potential uses for a smart toilet seat is broad, as long as privacy and control over disclosure and data are warranted.

The Metaverse for Healthcare: Trends, Applications, and Future Directions of Digital Therapeutics for Urology.

In recent years, the emergence of digital therapeutics as a novel approach to managing conditions has garnered significant attention. This approach involves using evidence-based therapeutic interventions that are facilitated by high-quality software programs to treat, manage, or prevent medical conditions. The incorporation of digital therapeutics into the Metaverse has increased the feasibility of their implementation and application in all areas of medical services. In urology, substantial digital therapeutics are being produced and researched, including mobile apps, bladder devices, pelvic floor muscle trainers, smart toilet systems, mixed reality-guided training and surgery, and training and telemedicine for urological consultations. The purpose of this review article is to provide a comprehensive overview of the current impact of the Metaverse on the field of digital therapeutics and identify its current trends, applications, and future perspectives in the field of urology.

Prediction of Urinary Tract Infection in IoT-Fog Environment for Smart Toilets Using Modified Attention-Based ANN and Machine Learning Algorithms

UTI (Urinary Tract Infection) has become common with maximum error rates in diagnosis. With the current progress on DM (Data Mining) based algorithms, several research projects have tried such algorithms due to their ability in making optimal decisions and efficacy in resolving complex issues. However, conventional research has failed to attain accurate predictions due to improper feature selection. To resolve such existing pitfalls, this research intends to employ suitable ML (Machine Learning)-based algorithms for predicting UTI in IoT-Fog environments, which will be applicable to a smart toilet. Additionally, bio-inspired algorithms have gained significant attention in recent eras due to their capability in resolving complex optimization issues. Considering this, the current study proposes MFB-FA (Modified Flashing Behaviour-based Firefly Algorithm) for feature selection. This research initializes the FF (Firefly) population and interchanges the constant absorption coefficient value with the chaotic maps as the chaos possesses an innate ability to evade getting trapped in local optima with the improvement in determining global optimum. Further, GM (Gaussian Map) is taken into account for moving all the FFs to a global optimum in an individual iteration. Due to such nature, this algorithm possesses a better optimization ability than other swarm intelligence approaches. Finally, classification is undertaken by the proposed MANN-AM (Modified Artificial Neural Network with Attention Mechanism). The main intention for proposing this network involves its ability to focus on small and significant data. Moreover, ANNs possess the ability for learning and modelling complex and non-linear relationships, in which the present study considers it. The proposed method is compared internally by using Random Forest, Naive Bayes and K-Nearest Neighbour to show the efficacy of the proposed model. The overall performance of this study is assessed with regard to standard performance metrics for confirming its optimal performance in UTI prediction. The proposed model has attained optimal values such as accuracy as 0.99, recall as 0.99, sensitivity as 1, precision as 1, specificity as 0.99 and f1-score as 0.99.

Urinary Biomarkers and Point-of-Care Urinalysis Devices for Early Diagnosis and Management of Disease: A Review.

Biosensing and microfluidics technologies are transforming diagnostic medicine by accurately detecting biomolecules in biological samples. Urine is a promising biological fluid for diagnostics due to its noninvasive collection and wide range of diagnostic biomarkers. Point-of-care urinalysis, which integrates biosensing and microfluidics, has the potential to bring affordable and rapid diagnostics into the home to continuing monitoring, but challenges still remain. As such, this review aims to provide an overview of biomarkers that are or could be used to diagnose and monitor diseases, including cancer, cardiovascular diseases, kidney diseases, and neurodegenerative disorders, such as Alzheimer's disease. Additionally, the different materials and techniques for the fabrication of microfluidic structures along with the biosensing technologies often used to detect and quantify biological molecules and organisms are reviewed. Ultimately, this review discusses the current state of point-of-care urinalysis devices and highlights the potential of these technologies to improve patient outcomes. Traditional point-of-care urinalysis devices require the manual collection of urine, which may be unpleasant, cumbersome, or prone to errors. To overcome this issue, the toilet itself can be used as an alternative specimen collection and urinalysis device. This review then presents several smart toilet systems and incorporated sanitary devices for this purpose.

Stool Image Analysis for Digital Health Monitoring By Smart Toilets

Gastrointestinal (GI) conditions are widespread and significantly impact the quality of life and healthcare. Stool appearance is a valuable GI diagnostic indicator, and with the advent of the Internet of Things (IoT), daily monitoring of excreta from a toilet is emerging as a promising digital health tool. This article describes a stool image analysis approach that classifies two physiologically relevant stool characteristics: 1) form and 2) color for an IoT-based smart toilet. We constructed a stool image data set with 3275 images, spanning all seven types of the Bristol stool-form scale (BSFS), a widely used metric for stool consistency and a variety of colors. We used ground-truth data obtained through the annotation of our data set by two gastroenterologists and developed a stool-color card to standardize the labeling of stool colors. We addressed two limitations associated with the application of computer-vision techniques to a smart-toilet system: 1) uneven separability between different stool-form categories and 2) class imbalance in the data set. We present results on hierarchical convolutional neural network (CNN) architectures for training a stool-form classifier and on perceptual color quantization coupled with machine-learning techniques to optimize the color-feature space for the classification of stool color. We utilized an edge–cloud approach to pursue an optimal balance between accuracy and latency and for the classification of stool form, we achieved a balanced accuracy of 84.4% and 84.2% reduction in latency compared to a cloud-model only. For color classification, the logistic-regression (LR) classifier provided 80.8% balanced accuracy and 47.3% reduction in communication latency.

Passive monitoring by smart toilets for precision health.

Smart toilets are a key tool for enabling precision health monitoring in the home, but such passive monitoring has ethical considerations.

Deep-Learning-Driven Proactive Maintenance Management of IoT-Empowered Smart Toilet

The recent proliferation of Internet of Things (IoT) sensors has driven a myriad of industrial and urban applications. Through analyzing massive data collected by these sensors, the proactive maintenance management can be achieved such that the maintenance schedule of the installed equipment can be optimized. Despite recent progress in proactive maintenance management in industrial scenarios, there are few studies on proactive maintenance management in urban informatics. In this article, we present an integrated framework of IoT and cloud computing platform for the proactive maintenance management in smart city. Our framework consists of: 1) an IoT monitoring system for collecting time-series data of operating and ambient conditions of the equipment and 2) a hybrid deep learning model, namely, convolutional bidirectional long short-term memory (CBLM) model for forecasting the operating and ambient conditions based on the collected time-series data. In addition, we also develop a naïve Bayes classifier to detect abnormal operating and ambient conditions and assist management personnel in scheduling maintenance tasks. To evaluate our framework, we deployed the IoT system in a Hong Kong public toilet, which is the first application of proactive maintenance management for a public hygiene and sanitary facility to the best of our knowledge. We collected the sensed data more than 33 days (808 h) in this real system. Extensive experiments on the collected data demonstrated that our proposed CBLM outperformed six traditional machine learning algorithms.

An Orthogonal Bidirectional Antenna Radar Sensing System for Smart Toilets

An Orthogonal Bidirectional Antenna Radar Sensing System for Smart Toilets