DESC:Field of the Invention
(0002) The present invention relates to the field of human health and behavioral monitoring technologies, and more specifically to systems, methods, and computer program products for non-invasive, continuous, and passive monitoring of physiological parameters and behavioral patterns in humans. The invention falls within the domains of biomedical engineering, digital health, ambient intelligence, wearable and non-wearable sensor systems, and smart environments, and is particularly relevant to applications in home healthcare, eldercare, rehabilitation, chronic disease management, and preventive medicine.
Background of the Invention
(0003) In recent years, there has been a growing need for continuous and non-invasive monitoring of human health and behavior, particularly in the context of elderly care, chronic disease management, post-operative recovery, and mental health tracking. Traditional health monitoring techniques, such as wearable devices, manual assessments, or periodic clinical evaluations, often suffer from several limitations including user discomfort, compliance issues, data inconsistency, and lack of real-time feedback.
(0004) Existing wearable technologies like smartwatches, fitness trackers, or medical-grade monitors require active engagement, regular charging, and consistent usage by individuals, which is often impractical for elderly, cognitively impaired, or bedridden patients. Moreover, these systems may fail to capture contextual behavioral patterns, such as frequency of bathroom use, time spent resting, eating habits, or nighttime mobility—factors that are critical indicators of deteriorating health or emerging complications.
(0005) Furthermore, manual observation methods—such as caregiver logs, camera-based surveillance, or periodic nurse check-ins—are either labor-intensive, privacy-intrusive, or reactive rather than preventive. These approaches lack the ability to capture granular, timestamped physiological data and are unable to deliver early warnings or trend analysis that could prevent hospitalizations or detect emerging disorders.
(0006) Therefore, there is a significant unmet need for an intelligent, passive, and integrated system that can unobtrusively monitor key behavioral and physiological parameters of humans in real-world, day-to-day environments. Such a system should be automated, privacy-preserving, and capable of real-time data analysis and alerting, thereby enabling proactive and personalized health interventions.
Summary of the Invention
(0007) The present invention provides an intelligent, passive, and non-intrusive monitoring system for tracking human behavioral patterns and physiological parameters through the integration of sensor-enabled platforms within commonly used household and clinical objects such as beds, chairs, toilet seats, flooring, and dining surfaces.
(0008) The system comprises a network of embedded sensors (e.g., load cells, pressure sensors, motion detectors, temperature sensors, and biosensors), a processing unit, a data storage and analytics engine, and a communication interface for real-time data transmission and remote access. These components work collaboratively to detect presence, measure body weight, record time-stamped activity durations, evaluate posture, and monitor other biometric or behavioral trends without requiring active input from the user.
(0009) The invention is designed to identify abnormal patterns, such as sudden changes in body weight, reduced mobility, increased sedentary behavior, disturbed sleep cycles, or prolonged toilet use, and can trigger automated alerts to caregivers, healthcare providers, or monitoring platforms.
(0010) A unique feature of the invention is its multi-point behavioral profiling capability, which allows it to learn individual routines over time and apply predictive analytics to detect early deviations from baseline health trends. The system is adaptable for multi-user environments and supports cloud-based data synchronization, enabling population-scale health monitoring.
(0011) The invention aims to enhance patient safety, promote preventive care, reduce caregiver burden, and support remote health surveillance—especially for the elderly, post-operative patients, individuals with chronic conditions, or those with mobility or cognitive impairments.
Detailed Description of the Invention
(0012) The present invention relates to an intelligent system for passively monitoring behavioral and physiological characteristics of humans using embedded sensor platforms integrated into daily-use objects in both residential and institutional care settings.
This system is designed to enable continuous, non-invasive, and automated health surveillance, offering real-time data analytics, trend detection, and early warnings—all while maintaining user comfort and privacy.
I. System Architecture
Figure 1: System Block Diagram
This diagram illustrates the integrated components of the system:
• 101 – Embedded Sensor Module (pressure, temperature, proximity, weight, motion)
• 102 – Microcontroller/Processor Unit
• 103 – Data Storage and Edge Analytics Unit
• 104 – Wireless Communication Interface (Wi-Fi/Bluetooth/LoRaWAN)
• 105 – Power Management Unit (Battery/AC/Harvesting)
• 106 – Cloud Analytics and Monitoring Dashboard
• 107 – Alerting Interface (SMS/Email/Push Notification)
These components work in unison to record, process, and communicate human activity and physiological signals without user intervention.
II. Preferred Embodiments
A. Smart Bed Platform
• Embedded load cells (beneath mattress support) continuously monitor bed occupancy, duration of stay, micro-movements, and weight trends.
• An accelerometer module can assess restlessness, sleep posture, and potential falls.
• Data is used to compute a sleep quality index, monitor recovery, or detect signs of deterioration (e.g., in post-operative or palliative care).
B. Intelligent Toilet Seat
• Equipped with pressure sensors, load cells, and bioimpedance sensors.
• Measures:
o Duration and frequency of toilet visits
o Body weight
o Postural stability
o Optionally: urine conductivity, skin temperature, or hydration index
• Alerts are sent if prolonged toilet time or rapid weight loss is detected.
III. Alternative Embodiments
A. Smart Dining Chair or Table
• Sensors embedded in seat or table surface track:
o Eating frequency
o Body position during meals
o Time spent seated at dining area
• Can be used to monitor appetite changes, especially in dementia or elderly care.
B. Ambient Floor Sensor Grid
• Pressure sensors under flooring detect:
o Gait patterns
o Stride length
o Fall events
o Inactivity periods
• Suitable for assisted living centers, capturing longitudinal data on mobility degradation.
C. Couch or Living Area Monitoring
• Monitors sedentary behavior, detects extended sitting durations, which may indicate depression or early functional decline.
IV. Data Processing and Intelligence Layer
• The system includes on-device analytics and cloud-based machine learning algorithms for:
o Anomaly detection (e.g., decreased movement)
o Pattern recognition (e.g., disrupted sleep)
o Behavioral profiling and clustering
o Predictive alerts for care escalation
• Advanced models compare current activity against historical baselines, and optionally against population norms to suggest personalized interventions.
V. Multi-User and Scalable Design
• The system supports multi-individual identification through a combination of:
o Weight profiles
o Interaction time patterns
o Optional: RFID tags, voiceprints, or proximity devices
• Suitable for nursing homes, hospitals, smart homes, and even corporate wellness programs.
VI. Power, Communication, and Privacy
• Power options: rechargeable battery, plug-in adapter, or energy harvesting (e.g., piezoelectric flooring).
• Communication: Wi-Fi, Bluetooth, ZigBee, NB-IoT, or LoRaWAN, depending on the environment.
• Privacy safeguards:
o No cameras or microphones
o Data encryption
o User access controls
I. System Overview
Figure 1: System Block Diagram
• 101 – Embedded Sensor Module: Pressure, temperature, proximity, weight, and motion sensors capture interaction data.
• 102 – Microcontroller/Processor Unit: Controls data flow, processes inputs, and runs decision logic.
• 103 – Data Storage & Edge Analytics Unit: Performs initial trend analysis, filtering, and secure data caching.
• 104 – Wireless Communication Interface: Enables transmission via Wi-Fi, Bluetooth, or LoRaWAN.
• 105 – Power Management Unit: Supports AC, battery, or energy harvesting operation.
• 106 – Cloud Analytics and Monitoring Dashboard: Receives data for longitudinal tracking and comparison.
• 107 – Alerting Interface: Pushes SMS/email/app notifications to stakeholders.
II. Preferred Embodiment 1: Smart Bed Sensor Platform
Figure 2: Smart Bed Sensor Integration
• 201 – Load Cells: Installed under each mattress corner to detect body weight and micro-movements.
• 202 – Accelerometer: Mounted on the bed frame to assess movement frequency and posture shifts.
• 203 – Sleep Movement Profile: Visualized on the cloud dashboard, indicating rest quality, duration, and disturbances.
This configuration enables detection of sleep disorders, early frailty, or decline in mobility.
III. Preferred Embodiment 2: Toilet-Seat Monitoring System
Figure 3: Toilet-Seat Monitoring Assembly
• 301 – Load Cell Ring: Embedded beneath the seat to measure user weight and session duration.
• 302 – Motion Sensor: Tracks sit/stand transitions and potential instability.
• 303 – Bluetooth Module: Installed inside the tank compartment to securely transmit local data to nearby receivers.
It helps detect urinary retention, dehydration, weight loss, or even postural imbalance in early stages.
IV. Preferred Embodiment 3: Cloud-Based Visualization Dashboard
Figure 4: Cloud-Based Dashboard View
• 401 – Behavioral Timeline: Shows daily activity distribution such as bed occupancy, toilet usage, and sedentary time.
• 402 – Alert Log: Color-coded list of anomaly triggers (green: normal; yellow: warning; red: critical).
• 403 – Longitudinal Graphs: Displays trends in weight, sleep patterns, toilet visits, and overall movement over time.
Clinicians and caregivers use this interface for remote health assessment, trend analysis, and intervention planning.
V. Overall System Deployment Illustration
Figure 5: Integrated 3D Use Case View
This figure presents a comprehensive overview of how sensors are deployed across:
• Smart Bed
• Toilet Unit
• Dining Table
• Living Room Chair
• Floor Sensor Grid
All components link to a central cloud-based analytics platform, enabling real-time health monitoring in home or institutional settings.
(0013) Examples
Example 1: Monitoring an Elderly Individual Living Alone
An 82-year-old woman resides independently in a smart home equipped with the invention.
• Smart Bed: Load cells (201) under her bed detect prolonged immobility during the night. The sleep movement profile (203) shows reduced turning, suggesting discomfort or health deterioration.
• Toilet Seat Sensor: A load cell ring (301) records unusually long toilet visits over two consecutive days. This, combined with a drop in body weight detected during sit-down events, triggers a yellow alert via the alerting interface (107).
• Outcome: Her caregiver receives an SMS notification and schedules a doctor’s appointment, where a urinary tract infection is diagnosed early and treated promptly.
Example 2: Fall Risk Detection in a Stroke Rehabilitation Patient
A 64-year-old man recovering from a stroke uses a rehabilitation bed and smart flooring with the embedded monitoring system.
• Motion Sensors: Pressure-sensitive floor tiles (motion sensor grid) detect an abnormal gait pattern, with a shorter stride and frequent instability over the last 48 hours.
• Accelerometer Data: Embedded in his chair, the accelerometer (202) shows abrupt posture shifts and near-falls.
• Cloud Dashboard: The analytics dashboard (Figure 4) flags a deviation from baseline walking rhythm and generates a red-level alert for physiotherapy reevaluation.
• Outcome: His therapy schedule is modified to include more balance training, reducing the likelihood of a fall.
Example 3: Nutrition and Activity Monitoring in a Dementia Patient
A 78-year-old woman with early-stage Alzheimer’s is monitored in an assisted living facility.
• Dining Surface Sensor: A pressure plate embedded in the dining table logs meal durations and frequency.
• Bed and Toilet Sensors: Bed sensors show decreased night-time rest and toilet sensors indicate irregular waste elimination patterns.
• Behavioral Timeline (401): Analysis over two weeks indicates she is skipping breakfast and exhibiting a disrupted circadian rhythm.
• Outcome: The care team is alerted and adapts her routine by providing nutritional shakes in the mid-morning and implementing calming sleep protocols.
Example 4: Remote Monitoring of Post-Surgery Recovery at Home
A 52-year-old patient recovering from a hernia operation is discharged with passive monitoring.
• Smart Bed Module: Records night-time restlessness and reports increased frequency of sleep interruptions.
• Chair Sensor: Detects she is sitting for over 12 hours/day with minimal weight shifts—an early indicator of low mobility.
• Outcome: A warning is triggered for sedentary behavior and poor recovery progress. Her physician recommends light home physiotherapy and follow-up.
Example 5: Workplace Wellness Monitoring (Optional Use Case)
A tech company installs sensor-equipped chairs and flooring in a high-stress department to monitor employee wellness.
• Embedded Load Cells: Detect prolonged sitting without breaks, while activity graphs (403) from the dashboard show declining movement.
• Outcome: HR uses anonymized reports to recommend breaks, schedule ergonomic consultations, and promote wellness interventions.
(0014) Advantages of the Invention
The present invention offers several significant advantages and improvements over prior art in the domain of human health monitoring systems:
1. Fully Passive and Non-Intrusive Monitoring
• Unlike wearables or implantable devices, the invention requires no active participation or behavioral change from the user.
• Sensor modules are embedded into familiar daily-use objects (beds, toilets, chairs, floors, dining surfaces), thereby maintaining user dignity, comfort, and routine.
2. Continuous, Real-Time Data Collection
• Enables round-the-clock monitoring without the limitations of battery life, charging, or device adherence associated with traditional health wearables.
• Provides granular, timestamped datasets that allow for precise behavioral and physiological trend detection.
3. Early Detection of Health Deterioration
• Tracks micro-changes in parameters such as body weight, toilet frequency, movement patterns, posture, and rest quality.
• Employs anomaly detection and predictive analytics to raise early warnings—often before symptoms become observable—enabling preventive care.
4. Multi-Domain Functionality from a Unified System
• Monitors a broad range of metrics:
o Behavioral (sleep, sedentary behavior, eating routines)
o Physiological (weight, waste output, hydration proxy)
o Environmental interaction (sit/stand patterns, gait irregularities)
• Reduces the need for multiple, disconnected medical devices.
5. Scalability and Multi-Person Support
• Supports multi-user identification in households or care facilities using weight profiles, activity patterns, and behavioral signatures.
• Can scale to institutional settings (e.g., nursing homes, rehab centers, hospitals) through cloud integration and dashboard interfaces.
6. Automated Alerts and Caregiver Engagement
• Generates real-time alerts via SMS, push notifications, email, or integrated health dashboards.
• Allows remote caregivers or clinicians to be promptly informed and take data-driven action.
7. Privacy-Preserving by Design
• Unlike video surveillance or microphone-based monitoring, the system avoids invasive sensors, using instead pressure, motion, and weight data to infer health states.
• Offers a high-trust alternative in environments where privacy and discretion are essential.
8. Adaptive and Customizable
• Alert thresholds, monitoring rules, and analytical models can be tailored per individual based on baseline behavior and clinical needs.
• Modular design allows selective deployment based on user requirements (e.g., only bed and toilet monitoring for some, full-suite monitoring for others).
9. Applicable Across Age Groups and Clinical Conditions
• Beneficial for:
o Elderly adults (fall risk, frailty)
o Post-surgical patients (recovery monitoring)
o Neurological/psychiatric patients (sleep and mobility tracking)
o People with disabilities (activity and independence metrics)
o Chronic disease patients (hypertension, heart failure, diabetes)
10. Integration with Telemedicine and EHR Systems
• The cloud-based infrastructure can synchronize with Electronic Health Records (EHRs) or telehealth platforms, promoting seamless remote care coordination.
,CLAIMS:Claims
Independent Claims
Claim 1 An intelligent system for passive monitoring of human behavioral and physiological characteristics, the system comprising:
• one or more embedded sensor modules configured to be integrated into daily-use objects including beds, chairs, toilet seats, flooring, or tables, the sensor modules comprising at least one of a pressure sensor, weight sensor, temperature sensor, motion detector, or proximity sensor;
• a microcontroller or processor unit operatively coupled to the sensor modules and configured to process incoming signals;
• a data storage and edge analytics unit configured to store sensor data and perform real-time or near real-time data analysis;
• a wireless communication interface for transmitting data to a remote monitoring system;
• a power management unit operable via AC power, battery, or energy harvesting techniques; and
• a cloud-based analytics dashboard configured to receive, store, analyze, and visualize user-specific behavioral and physiological patterns;
• wherein the system is configured to generate alerts when deviations from a learned baseline are detected.
Claim 2 The system of Claim 1, wherein the embedded sensor modules are configured to passively monitor users without requiring user input or wearable devices.
Claim 3 The system of Claim 1, wherein the cloud-based analytics dashboard includes:
• a behavioral timeline,
• an alert log with color-coded severity levels, and
• longitudinal trend graphs for parameters including weight, sleep quality, toilet usage, or activity levels.
Dependent Claims
Claim 4 The system of Claim 1, wherein the smart bed sensor module comprises:
• load cells positioned under each corner of a bed frame to measure body weight and movement patterns; and
• an accelerometer mounted to detect posture shifts or signs of restlessness.
Claim 5 The system of Claim 1, wherein the toilet seat sensor module includes:
• a ring-shaped load cell beneath the seat for weight and usage duration measurement;
• a motion sensor for sit/stand detection; and
• a wireless transmission module housed in the tank compartment.
Claim 6 The system of Claim 1, further comprising a floor sensor grid installed beneath floor tiles and configured to measure:
• gait parameters including stride length and frequency;
• pressure distribution; and
• fall detection events based on abnormal impact or motion signatures.
Claim 7 The system of Claim 1, wherein the microcontroller is configured to compare measured data against threshold values and trigger alerts via:
• SMS,
• email, or
• mobile application notifications.
Claim 8 The system of Claim 1, wherein the system is capable of identifying individual users based on:
• body weight signatures,
• interaction time patterns, and
• behavioral routines such as eating time, sleep schedules, or toilet frequency.
Claim 9 The system of Claim 1, wherein data collected from multiple sensor modules is synchronized in real-time and stored in a secure, privacy-compliant database for caregiver or clinician access.
Claim 10 The system of Claim 1, wherein the edge analytics unit utilizes machine learning models to:
• detect anomalies,
• adapt to user-specific baselines, and
• predict emerging health risks such as frailty, sleep disorders, or infection.