DESC:Field of the Invention
[0001] The present invention relates to the field of biomedical engineering and healthcare monitoring systems, and more specifically to a wearable medical device for real-time monitoring of posture and mobility in bedridden patients. The invention further pertains to systems and methods for fall risk prediction and prevention of pressure ulcers (bedsores) using a smart back-wear sensor integrated with wireless communication and predictive analytics. It finds application in hospitals, nursing homes, intensive care units, and home healthcare settings, particularly for improving patient safety and adherence to clinical repositioning protocols.
Background of the Invention
[0002] In clinical settings such as hospitals, long-term care facilities, and rehabilitation centers, bedridden patients are at significant risk of developing pressure ulcers (also known as bedsores or decubitus ulcers) due to prolonged immobility and sustained pressure on specific parts of the body. These injuries can lead to serious complications including infections, extended hospital stays, and even mortality.
[0003] To prevent pressure ulcers, standard clinical practice mandates regular repositioning of patients every 2 to 3 hours. However, this is often manually monitored and documented by nursing staff, which is prone to human error, documentation gaps, and inconsistent compliance. Additionally, existing pressure ulcer prevention systems are either passive (like specialized mattresses) or require manual input, lacking real-time posture detection or automated alerts for missed turns.
[0004] Another major concern in immobile or semi-mobile patients is the risk of accidental falls, especially when patients attempt to move without assistance. Existing fall prevention systems, such as motion sensors or bed alarms, are reactive in nature—they alert caregivers only after a fall or risky movement has occurred, rather than providing proactive warning based on posture or mobility trends.
[0005] Some wearable technologies exist for activity tracking, but they are generally not designed for continuous supine posture monitoring, are not optimized for bedridden use, or lack integration with clinical protocols for repositioning. Moreover, they often generate false positives due to placement on limbs or dynamic body parts, especially when skin folds or bedding interfere with sensor readings.
[0006] Therefore, there is a critical need for a smart, sensor-based system that can:
• Monitor the real-time posture of bedridden patients,
• Predict and alert caregivers about fall risk before incidents occur,
• Automatically log body position changes to ensure protocol compliance,
• Be comfortably worn on the upper back, minimizing interference and maximizing accuracy.
[0007] The present invention addresses these challenges by introducing a smart back-wear sensor system designed specifically for accurate posture detection and predictive fall risk monitoring in immobilized patients. This invention aims to bridge the gaps in current care practices by leveraging wearable technology, wireless communication, and intelligent analytics to improve patient outcomes and reduce caregiver burden.
Summary of the Invention
[0008] The present invention provides a smart back-wear sensor system designed for continuous, real-time monitoring of posture and mobility in bedridden patients, with integrated features for fall risk prediction and pressure ulcer prevention. The system comprises a compact, wearable sensor module embedded with an accelerometer, gyroscope, and wireless transceiver, affixed to the upper back of the patient—between the scapulae—to ensure accurate detection of body orientation and minimize false readings caused by soft tissue movement.
[0009] The sensor continuously captures data on body posture and movement, which is transmitted wirelessly to a patient monitor unit. The monitor processes the data using predefined clinical repositioning protocols to:
• Automatically detect and log postural changes (e.g., left lateral, right lateral, supine),
• Alert caregivers when repositioning intervals are exceeded,
• Monitor compliance with turn schedules,
• Identify non-compliant movement patterns such as attempts to leave the bed without assistance.
[0010] The invention also includes fall risk assessment algorithms that analyze posture trends, movement patterns, and duration of immobility to predict and issue preemptive alerts to caregivers, reducing the likelihood of accidents. Additionally, the system supports multi-parameter sensing, including optional modules for temperature, ECG, and acoustic data, enabling a holistic monitoring approach.
[0011] Key objectives of the invention are:
• To automate posture monitoring and reduce dependence on manual logs,
• To prevent pressure ulcers through timely alerts and protocol-based turn tracking,
• To predict and prevent patient falls using proactive analytics,
• To ensure seamless integration with hospital networks and caregiver workflows.
[0012] This innovative system is particularly suited for deployment in ICUs, geriatric wards, home care settings, and post-operative care units, where continuous supervision is critical but often limited. The invention significantly enhances patient safety, clinical efficiency, and quality of care through intelligent, sensor-driven intervention.
Detailed Description of the Invention
[0013] The present invention provides a smart wearable sensor system designed for continuous monitoring of posture and mobility in bedridden patients, aimed at preventing pressure ulcers and reducing fall risks. The core of the system consists of a compact, ergonomically designed sensor housing that includes an accelerometer, gyroscope, wireless transceiver, and optionally, sensors for temperature, ECG, or acoustic signals.
Preferred Embodiment
[0014] In a preferred embodiment, the sensor unit is configured as an adhesive-backed, oval-shaped patch. The patch is attached to the patient's upper back, specifically between the scapulae, an area chosen to minimize erroneous readings due to movement of fatty or loose tissue elsewhere on the body.
[0015] The internal sensor circuitry comprises:
• A triaxial accelerometer for capturing orientation and tilt
• A gyroscope for angular velocity and movement tracking
• A Bluetooth Low Energy (BLE) transceiver for wireless communication
• A microcontroller with signal processing capability
• A rechargeable battery or contactless power supply (optional)
[0016] When in use, the sensor captures real-time posture data and transmits it to a dedicated patient monitor device, which may be placed at the bedside or integrated into the hospital’s central monitoring network. The monitor compares the incoming posture data against a preset repositioning protocol (e.g., turn every 2 hours) and raises an alert if:
• The patient has not changed position within the allotted time window
• The detected movement does not meet the threshold for a valid positional change
[0017] Additionally, the system logs every patient turn and calculates:
• The number of successful turns in a 24-hour window
• The duration spent in each position
• Missed or delayed repositioning events
[0018] Fall risk prediction is enabled through analysis of movement acceleration patterns and deviations in regular mobility trends. For instance, an abrupt shift from supine to a vertical tilt without prior micro-movements can indicate an attempt to get out of bed, prompting an early warning.
Alternative Embodiments
[0019] In an alternative embodiment, the sensor includes additional modules such as:
• Temperature sensors for detecting early signs of infection or inflammation
• ECG sensors to provide basic cardiac monitoring
• Acoustic sensors for detecting distress sounds or coughing patterns
[0020] The sensor can also be integrated into clothing or specialized hospital garments. In such cases, the electronics are embedded within washable, flexible materials with detachable battery packs.
[0021] In yet another embodiment, the sensor communicates not only with a bedside monitor but also with a cloud-based analytics platform for real-time data processing and storage. This enables predictive modeling, caregiver reporting, and historical trend analysis.
[0022] To support multi-patient monitoring, a nurse dashboard interface is provided, showing status summaries, alerts, and compliance metrics. Each sensor is uniquely identified and securely paired with the patient’s ID using a proximity-based pairing protocol (<3 inches range).
Brief Description of the Drawings
[0024] The invention will now be described in more detail with reference to the accompanying drawings, which illustrate exemplary embodiments of the invention and are not intended to limit its scope.
• FIG. 1 illustrates a schematic view of the smart wearable sensor unit showing its internal components, including an accelerometer, gyroscope, Bluetooth Low Energy (BLE) module, and battery, all housed within a compact unit configured for back-wear application.
• FIG. 2 shows the anatomical placement of the wearable sensor on a patient’s back, specifically positioned between the scapulae. This central upper-back location is optimal for detecting changes in body posture and minimizing interference from soft tissue.
• FIG. 3 is a block diagram of the system architecture, illustrating the data flow from the wearable sensor to the patient monitor and subsequently to the cloud. This enables real-time monitoring, historical data logging, and predictive analytics via remote interfaces.
• FIG. 4 presents an example of a nurse dashboard interface that receives real-time alerts from the patient monitor. The dashboard displays current posture, fall risk status, and alert conditions for multiple patients, facilitating proactive care management.
Examples
[0025] The following examples are provided to illustrate the operation of the invention in various healthcare scenarios. These examples are not intended to limit the scope of the invention, but to demonstrate preferred embodiments in practical use.
Example 1: Pressure Ulcer Prevention in an ICU Setting
[0026] A 68-year-old male patient admitted to the Intensive Care Unit (ICU) with acute stroke was bedridden and non-verbal. A smart back-wear sensor unit was applied between the scapulae upon admission. The sensor transmitted continuous posture data to a bedside patient monitor, which was linked to the hospital's central nurse dashboard.
Within 3 hours, the system detected that the patient had not changed position and issued an automated alert to the assigned nurse. On review, it was found that the repositioning protocol had been unintentionally skipped during the shift change. The nurse responded immediately, repositioned the patient, and the turn was logged automatically in the patient monitor. This prevented potential formation of pressure ulcers, improving protocol adherence and reducing manual charting.
Example 2: Fall Risk Alert in a Geriatric Ward
[0027] A 74-year-old female recovering from hip replacement surgery was fitted with the sensor. On the second post-operative day, the system recorded a sharp change in tilt angle and linear acceleration without gradual buildup—a profile consistent with unassisted bed-exit attempts. The system issued a real-time fall risk alert to the nurse station and mobile caregiver device. A caregiver responded within 1 minute and found the patient standing near the bed in an unstable posture. The incident was averted. The system also generated a report identifying three similar pre-fall patterns during the 24-hour period, leading to a clinical decision to increase observation frequency.
Example 3: Compliance Tracking in a Long-Term Care Facility
[0028] A long-term care resident was under a 2-hour repositioning protocol due to prior history of pressure ulcers. The system logged every valid positional change, categorizing them into supine, left lateral, right lateral, and upright postures.
At the end of the 24-hour period, the nursing supervisor used the dashboard interface (FIG. 4) to view a summary report indicating:
• Total turns: 10
• Missed turn alert: 1 (flagged during night shift)
• Time in supine position: 7 hours
• Average repositioning interval: 2.3 hours
This report was attached to the patient’s EHR and shared with the attending physician during rounds, improving transparency and protocol compliance.
Example 4: Remote Monitoring via Cloud Interface
[0029] In a home healthcare scenario, a 55-year-old paraplegic patient used the device while receiving home-based care. The sensor data was uploaded to a secure cloud platform, accessible by both the caregiving nurse and the remote supervising physician. When the patient remained in a reclined position for more than 5 hours without any postural change, the cloud-based system generated an automated SMS and email alert. The caregiver received it in real-time, promptly repositioned the patient, and avoided complications. The system also provided trend analytics showing reduced mobility patterns, prompting re-evaluation of the patient's care plan.
Advantages of the Invention
[0030] The present invention offers several significant advantages over existing technologies and manual care practices. The smart back-wear sensor system provides a comprehensive, intelligent, and automated approach to posture monitoring and fall risk prediction in bedridden patients. The key advantages are as follows:
1. Accurate Posture Monitoring from a Stable Anatomical Location
Unlike existing wearable devices attached to limbs or hips, the sensor is placed centrally between the scapulae, reducing motion artifacts from soft tissue and improving the accuracy of posture detection.
2. Automated Logging of Patient Turns
The system eliminates the need for manual charting by caregivers. It automatically detects and logs repositioning events, ensuring compliance with clinical protocols for pressure ulcer prevention.
3. Proactive Fall Risk Prediction
The system uses real-time sensor data and motion patterns to predict risky movements before a fall occurs, enabling preventive intervention rather than post-fall alerts.
4. Reduction in Human Error and Missed Protocols
By using an intelligent alerting mechanism and dashboard interface, the invention notifies caregivers when repositioning is missed, addressing one of the major gaps in manual systems.
5. Multi-Parameter Monitoring Capability
The wearable unit supports integration with additional sensors (temperature, ECG, acoustic), enabling multi-modal patient assessment from a single device.
6. Wireless Communication and Remote Access
The BLE-based wireless system enables seamless data transfer to bedside monitors or cloud platforms, allowing remote monitoring by physicians and real-time alerts to caregivers.
7. Improved Patient Safety and Clinical Efficiency
By automating critical care tasks, reducing documentation burden, and enabling early intervention, the system improves patient outcomes, reduces nursing workload, and enhances workflow in both hospital and home settings.
8. Cost-Effective and Scalable
The invention uses low-power, commercially available sensors and supports multi-patient monitoring via dashboards, making it suitable for large-scale deployment in ICUs, geriatric wards, and long-term care facilities.
9. Customizable Protocol Integration
The system can be programmed to align with hospital-specific turn schedules, allowing flexibility and tailored care plans for individual patients.
10. Secure Patient Identification and Pairing
A proximity-based sensor pairing mechanism (<3 inches) ensures correct device-to-patient association, minimizing clinical errors due to misidentification.
[0031] These advantages make the invention a novel, practical, and superior solution compared to the prior art in the domains of wearable health technology, patient monitoring systems, and hospital workflow management.
,CLAIMS:Claims
Claim 1 (Independent Claim):
A wearable sensor system for monitoring posture and predicting fall risk in bedridden patients, the system comprising:
• a sensor unit configured to be removably attached to the upper back of a patient between the scapulae,
• the sensor unit comprising:
– an accelerometer configured to detect orientation and movement;
– a gyroscope configured to measure angular velocity;
– a wireless transceiver for transmitting sensor data; and
– a power supply;
• a patient monitor configured to receive data from the sensor unit, the monitor comprising:
– a signal processor for analyzing posture and movement patterns;
– a memory device for storing posture history and event logs; and
– an alert generator configured to notify caregivers when a repositioning protocol is violated or when fall risk exceeds a defined threshold;
wherein the system is adapted to automatically log postural changes and issue real-time alerts based on predefined clinical rules.
Claim 2:
The system as claimed in claim 1, wherein the sensor unit is housed in an ergonomically contoured enclosure having an adhesive backing for skin-safe attachment.
Claim 3:
The system as claimed in claim 1, wherein the sensor unit further comprises a microcontroller for preprocessing posture signals prior to wireless transmission.
Claim 4:
The system as claimed in claim 1, wherein the patient monitor is integrated with a nurse dashboard interface configured to display real-time posture status and compliance metrics for one or more patients.
Claim 5:
The system as claimed in claim 1, wherein the signal processor is configured to determine whether the patient has remained in the same position for longer than a predefined interval and to generate alerts accordingly.
Claim 6:
The system as claimed in claim 1, wherein the alert generator issues notifications via at least one of a visual signal, an audible signal, an SMS, or a mobile application.
Claim 7:
The system as claimed in claim 1, wherein the posture data includes classification of the patient’s body orientation as supine, prone, left lateral, right lateral, or upright.
Claim 8:
The system as claimed in claim 1, wherein the sensor unit includes one or more additional sensors selected from the group consisting of: temperature sensor, ECG sensor, and acoustic sensor.
Claim 9:
The system as claimed in claim 1, wherein the sensor unit is uniquely paired to the patient monitor using a short-range proximity-based pairing method.
Claim 10:
The system as claimed in claim 1, wherein the patient monitor is configured to transmit posture history and compliance data to a remote server or cloud-based analytics platform.
Claim 11:
The system as claimed in claim 1, wherein the fall risk is predicted by comparing motion trends and acceleration thresholds using a pre-trained predictive algorithm.
Claim 12:
The system as claimed in claim 1, wherein the patient monitor generates statistical summaries including average repositioning