Description:DESCRIPTION OF INVENTION
FIELD OF THE INVENTION
The present invention relates to a system and method for fall detection using a combination of sensors and physiological parameter monitoring.
More specifically, it employs an array of sensors along with machine learning algorithms to determine the cause of a fall and provide actionable insights for emergency response. The invention aims to enhance the accuracy of fall detection by incorporating multiple physiological and environmental parameters, ensuring timely medical intervention and assistance.
BACKGROUND OF THE INVENTION
Falls are a leading cause of injury and mortality among elderly individuals, individuals with neurological disorders, and patients suffering from chronic illnesses. The consequences of falls can be severe, often resulting in fractures, head trauma, or long-term disability. Due to the increasing aging population and the prevalence of chronic conditions, the burden of fall-related injuries on healthcare systems is growing exponentially. Immediate and accurate fall detection is crucial to reducing the severity of injuries and ensuring timely medical intervention.
Existing fall detection technologies primarily rely on motion-based sensors such as accelerometers and gyroscopes to detect rapid changes in movement or sudden impact. However, these systems often suffer from high false-positive rates, where everyday movements such as sitting down quickly, bending over, or dropping an object may be misinterpreted as falls. Additionally, these motion-based systems lack the capability to determine the underlying cause of a fall, which is critical for appropriate medical intervention.
Several wearable devices have attempted to integrate fall detection features using accelerometers combined with GPS tracking. While GPS tracking allows caregivers to locate the individual in case of a fall, it does not provide insights into physiological conditions leading up to the fall. More advanced systems incorporate smartphone-based applications, which use motion sensors to detect falls and send alerts to emergency contacts. However, such systems require the user to carry a smartphone at all times, which may not always be practical, especially for elderly individuals with cognitive impairments.
Another limitation of existing fall detection technologies is their inability to differentiate between falls caused by external environmental factors and those resulting from internal physiological disturbances. Falls can occur due to a wide range of health-related issues, including sudden drops in blood pressure, dehydration, hypoglycemia, cardiac arrhythmias, or neurological dysfunction. Without the ability to monitor these physiological factors, current systems fail to provide a comprehensive assessment of fall risk and causality.
In addition to detection, the response time following a fall is a critical factor in determining patient outcomes. Delays in emergency medical intervention can lead to complications such as hypothermia, pressure sores, or even death in severe cases. Current systems that send alerts to caregivers or emergency responders often do not provide sufficient context regarding the severity of the fall, leading to either unnecessary emergency responses or delayed medical attention when it is most needed.
Given these limitations, there is a pressing need for a more advanced and comprehensive fall detection system that integrates multiple physiological and environmental parameters, reduces false alarms, and provides meaningful insights for effective medical intervention. A system that can accurately assess the cause of a fall, differentiate between minor and severe incidents, and ensure timely response would significantly improve patient outcomes and reduce the burden on healthcare services.
The present invention addresses the shortcomings of the prior art and describes AI-driven multi-sensor fall detection and causality assessment system with intelligent emergency response.
OBJECTS OF THE INVENTION
The primary objective of the present invention is to provide a multi-sensor-based fall detection and causality assessment system that enhances the accuracy of fall detection and determines the underlying cause of falls using motion, physiological, and environmental parameters.
Further object of the invention is to analyze posture auto-correction failure, heart rate variability (HRV) fluctuations, persistent tachycardia, hydration status, and patient-specific historical health data to identify the precise reason for a fall.
Further object of the invention is to incorporate ECG-derived PR interval analysis for evaluating vagal tone variations, enabling the detection of neurological conditions contributing to falls.
Further object of the invention is to enhance fall classification accuracy by using artificial intelligence (AI)-driven algorithms, including Convolutional Neural Networks (CNNs) and Support Vector Regression (SVR), reducing unnecessary emergency responses;
Another object of the invention is to assist hospitals and emergency departments by providing pre-emptive notifications about incoming fall-related cases, allowing for better preparedness and optimized resource allocation.
Yet another object of the invention is to create a reliable, AI-powered health monitoring system that enhances the safety of elderly individuals, patients with chronic illnesses, and individuals with neurological or cardiovascular conditions.
By achieving these objectives, the present invention significantly improves fall detection, medical response, and overall patient care, reducing morbidity and mortality associated with fall-related incidents.
SUMMARY OF THE INVENTION
Embodiments of the present disclosure present technological improvements as a solution to one or more of the above-mentioned technical problems recognized by the inventor in existing techniques.
The present invention relates to an AI-driven multi-sensor fall detection and causality assessment system with intelligent emergency response.
According to an aspect of the present invention, the present invention provides a multi-sensor fall detection and causality assessment system that enhances the accuracy of fall detection and determines the underlying cause of falls using motion, physiological, and environmental data. The system integrates an array of sensors, including gyroscopes, accelerometers, electrocardiogram (ECG), photoplethysmography (PPG), heart rate variability (HRV), temperature, and hydration sensors, to monitor real-time body movements, cardiovascular responses, and hydration levels.
According to further aspect of the present invention, a data processing unit collects and processes sensor data using artificial intelligence (AI)-driven algorithms, employing machine learning models such as Convolutional Neural Networks (CNNs) and Support Vector Regression (SVR) for precise fall event classification. The system performs fall cause analysis by assessing posture auto-correction failure, HRV fluctuations, persistent tachycardia, dehydration risks, and patient-specific historical health data, including diabetes, hypertension, and medication records. Additionally, ECG-derived PR interval analysis evaluates vagal tone variations to detect neurological conditions contributing to falls.
According to further aspect of the present invention, upon detecting a fall, the system automatically triggers alerts to caregivers and healthcare professionals via mobile notifications and sends emergency alerts to hospitals, ensuring that the necessary medical department is prepared in advance. GPS-based tracking locates the nearest available medical responders, optimizing emergency response times. Moreover, the system recommends personalized medical interventions, such as administering glucose for hypoglycemia-induced falls or initiating pain management for suspected fractures.
By integrating multi-sensor technology, AI-driven fall causality assessment, and automated emergency response, the present invention minimizes false alarms, ensures timely medical intervention, and improves patient safety and healthcare efficiency.
The objects and the advantages of the invention are achieved by the process elaborated in the present disclosure.
BRIEF DESCRIPTION OF DRAWINGS
The foregoing Summary, as well as the following detailed description of preferred embodiments of the invention, will be better understood when read in conjunction with the drawings as well as experimental results. The accompanying drawings constitute a part of this specification and illustrate one or more embodiments of the invention. Preferred embodiments of the invention are described in the following with reference to the drawings, which are for the purpose of illustrating the present preferred embodiments of the invention and not for the purpose of limiting the same. The objects and advantages of the present invention will become apparent when the disclosure is read in conjunction with the following figures, wherein
Figure 1 shows the Fall Detection System (100) which leverages a comprehensive network of sensors, including motion sensors (gyroscopes and accelerometers), physiological sensors (ECG, PPG, HRV), and environmental sensors (temperature and hydration monitors), to continuously monitor the user’s condition. Raw data from these sources is collected and processed through an advanced data processing unit to identify anomalies related to posture or physiological changes. If a potential fall is detected, the system proceeds to a fall cause analysis module to determine the underlying cause. Based on the severity of the situation, real-time alerts are sent to caregivers or emergency responders. Additionally, the system incorporates historical data and machine learning algorithms to improve detection accuracy over time by learning from past incidents;
Figure 2 illustrates Fall Cause Analysis Module (200) operates by first collecting sensor data that monitors body posture and physiological parameters. When posture auto-correction mechanisms fail, the system evaluates various physiological markers to pinpoint the underlying cause. Heart Rate Variability (HRV) and ECG readings are analyzed for cardiac-related incidents, while hydration levels derived from PPG data help detect dehydration-induced falls. PPG slope analysis can indicate hypoglycemia, whereas PR interval assessments highlight neurological risks. In cases of tachycardia, the system suggests potential pain or fracture-related causes. Cross-referencing with the user’s medical history and current medications provides further context, helping to assess the severity of the incident. Depending on the severity, the response may vary — severe cases trigger immediate emergency intervention, moderate cases notify caregivers, and mild cases focus on monitoring and preventive alerts. The system ensures that appropriate action is taken promptly, minimizing the impact of falls;
Figure 3 depicts the Personalized Intervention Module (300) integrates personalized risk profiling by drawing on previous fall data, the user’s medical history, and potential side effects from medications that may influence balance or alertness. This data is fed into an adaptive fall detection algorithm, enabling the system to improve its detection accuracy over time. Incident analysis and continuous learning help refine the algorithm further by evaluating each event for insights. Based on this evolving understanding, the system delivers customized alerts and prevention strategies tailored to the user’s needs. A continuous feedback loop ensures that the system adapts to changing health conditions, providing proactive and personalized fall prevention. This holistic approach not only detects falls but also aims to reduce their occurrence by offering insights and interventions tailored to each individual.
DETAILED DESCIPTION OF THE INVENTION
The following detailed description illustrates embodiments of the present disclosure and ways in which the disclosed embodiments can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.
The increasing prevalence of falls, particularly among elderly individuals and those with chronic conditions, underscores the critical need for accurate fall detection and timely intervention. Traditional fall detection systems primarily rely on motion sensors such as accelerometers and gyroscopes to identify abrupt changes in posture or impact forces. However, these methods alone are often insufficient in distinguishing between voluntary movements, accidental falls, and medical emergencies. Furthermore, existing solutions fail to provide insights into the physiological causes of falls, such as cardiovascular irregularities, dehydration, or medication-induced side effects. To address these limitations, the present invention introduces an AI-driven multi-sensor fall detection and causality assessment system with intelligent emergency response. By leveraging artificial intelligence-driven data processing and predictive analytics, the system not only detects falls but also evaluates their root causes, allowing for more informed emergency responses.
The present invention comprises a multi-sensor-based fall detection system designed to enhance accuracy in identifying fall events while providing actionable medical insights. The system integrates various sensing technologies, advanced data processing mechanisms, and intelligent analysis models to detect falls, determine their underlying causes, and trigger appropriate emergency responses. The embodiments of the invention include a comprehensive sensor array for motion, physiological, and environmental monitoring; a data processing unit that leverages artificial intelligence for real-time analysis; a fall cause analysis module that identifies contributing factors such as cardiac irregularities, dehydration, or neurological impairments; and an actionability and alert system that ensures timely medical intervention through automated notifications and responder coordination. By combining these components, the invention addresses the shortcomings of conventional fall detection systems, offering a more reliable and proactive approach to fall risk management.
1. Sensor Array:
The system comprises a comprehensive sensor array designed to accurately detect falls and assess physiological conditions.
Motion Sensors: The system incorporates gyroscopes and accelerometers to track body movements, sudden changes in posture, and impact forces. These sensors help distinguish between voluntary movements and actual falls.
Physiological Sensors: Electrocardiogram (ECG) sensors measure heart rate and electrical activity of the heart, photoplethysmography (PPG) sensors monitor blood oxygen levels and heart rate trends, and heart rate variability (HRV) sensors provide insights into autonomic nervous system function. These physiological parameters help determine whether a fall was caused by cardiovascular or neurological events.
Environmental Sensors: The system includes temperature sensors to detect external environmental conditions and hydration measurement sensors to evaluate fluid levels in the body. Hydration monitoring is crucial in assessing dehydration-related falls, especially in elderly individuals.
2. Data Processing Unit:
The system features a dedicated data processing unit that serves as the core computational hub for sensor data analysis. The unit continuously collects data from the sensor array and preprocesses it for feature extraction. AI-driven models are employed to analyze trends, detect anomalies, and assess fall risks in real-time.
The system utilizes cloud-based data storage to maintain historical records of physiological parameters and movement patterns. This long-term data collection enables predictive analytics, allowing the system to anticipate fall risks based on progressive changes in mobility patterns and health status.
3. Fall Cause Analysis:
The fall cause analysis module determines the underlying reason for a fall and evaluates the severity of the event.
Detection of Falls: The system identifies falls based on sensor inputs and evaluates whether the user attempted posture auto-correction. Failure to self-correct posture is a strong indicator of a true fall.
Physiological Correlation: Heart rate variability (HRV) fluctuations are monitored and correlated with ECG and PPG data to detect potential cardiac-related falls.
Detection of Tachycardia and Pain Indicators: The system identifies persistent tachycardia, which may indicate distress, pain, or potential fractures resulting from the fall.
Hydration Assessment: Hydration levels are evaluated to determine if dehydration contributed to the fall, particularly in individuals at risk of orthostatic hypotension.
Cross-Referencing Historical Health Data: The system integrates patient history, including diabetes, hypertension, and medication records, to assess additional risk factors contributing to falls.
4. Actionability & Alerts:
Once a fall event is detected and analyzed, the system initiates an emergency response protocol to ensure timely intervention.
Real-Time Caregiver Alerts: The system alerts caregivers through mobile notifications with real-time status updates regarding the fall event and the detected physiological anomalies.
Hospital Emergency Notifications: In severe cases, emergency alerts are sent directly to hospitals, and the necessary medical department is prepared in advance to receive and treat the patient.
GPS-Based Responder Routing: GPS tracking is integrated to identify the nearest available medical responders, ensuring that help reaches the individual as quickly as possible.
Medical Intervention Recommendations: The system provides specific medical recommendations based on the fall cause analysis. For example, if hypoglycemia is detected as a cause, the system suggests administering glucose, whereas a suspected fracture triggers pain management protocols.
By integrating these multi-sensor technologies, AI-based analysis, and real-time response mechanisms, the proposed invention significantly enhances the accuracy and effectiveness of fall detection and emergency response. This system ensures timely medical intervention while reducing false alarms, ultimately improving patient outcomes and reducing the burden on healthcare providers.
These embodiments are provided to demonstrate the various aspects and features of the AI-driven multi-sensor fall detection and causality assessment system with intelligent emergency response. It should be understood that the invention is not limited to these specific embodiments and can be implemented in different configurations and variations without departing from the scope of the invention as defined in the claims.
, Claims:CLAIMS:
We claim,
1. An AI-driven multi-sensor fall detection and causality assessment system (100) with intelligent emergency response, the said system comprising:
a) a sensor array configured to collect motion, physiological, and environmental data, the sensor array including:
- motion sensors comprising gyroscopes and accelerometers to detect body movements, posture changes, and impact forces;
- physiological sensors including electrocardiogram (ECG) sensors for cardiac activity monitoring, photoplethysmography (PPG) sensors for blood oxygen and heart rate assessment, and heart rate variability (HRV) sensors for autonomic nervous system analysis;
- environmental sensors comprising temperature sensors for ambient condition detection and hydration measurement sensors for fluid level assessment.
b) a data processing unit communicatively coupled with the sensor array, wherein the data processing unit: Preprocesses sensor data and extracts relevant motion and physiological features; Implements machine learning models for real-time fall detection, trend analysis, and anomaly identification; Utilizes cloud-based storage to maintain historical records for predictive analytics; A fall cause analysis module (200), wherein the module determines the underlying reason for a fall by; Detecting posture auto-correction failure to differentiate falls from voluntary movements; Analyzing HRV fluctuations in correlation with ECG and PPG data to identify cardiac-related falls; Detecting persistent tachycardia indicative of pain, distress, or fractures; Assessing hydration status to determine dehydration-induced falls; Cross-referencing patient history, including diabetes, hypertension, and medication records, to assess risk factors.\
c) an actionability and alert system, wherein the system: generates real-time notifications to caregivers via mobile applications; transmits emergency alerts to hospitals with automatic department-specific preparation; utilizes GPS-based tracking to locate the nearest medical responders and optimize emergency response time; provides personalized medical intervention recommendations based on fall cause analysis.
wherein the system employs an artificial intelligence-driven multi-parameter assessment model that integrates motion, physiological, and historical health data to not only detect falls but also determine their cause, severity, and appropriate emergency response, thereby significantly enhancing fall detection accuracy and post-fall intervention.
2. The multi-sensor fall detection and causality assessment system (100) as claimed in Claim 1, wherein the artificial intelligence models, including convolutional neural networks (CNNs) and support vector regression (SVR), are implemented to: Enhance fall detection accuracy by distinguishing between voluntary movements and actual falls; Classify falls based on severity and probable physiological causes; Predict future fall risks based on mobility pattern changes over time.
3. The multi-sensor fall detection and causality assessment system (100) as claimed in Claim 1, wherein: the system prioritizes emergency response based on severity scoring derived from multi-sensor data fusion; alerts are automatically sent to hospitals with preemptive department-specific readiness; the system integrates a GPS module for dynamic responder routing to the incident location.
4. The multi-sensor fall detection and causality assessment system (100) as claimed in Claim 1, wherein, PPG-derived metrics are utilized to determine dehydration-related falls; the system assesses orthostatic hypotension risks using hydration and heart rate variability correlation.
5. The multi-sensor fall detection and causality assessment system (100) as claimed in Claim 1, wherein the system analyzes PPG slopes and metabolic indicators to detect hypoglycemia-induced falls; alerts recommend immediate glucose administration in cases of low blood sugar-induced falls.
6. The multi-sensor fall detection and causality assessment system (100) as claimed in Claim 1, wherein the system continuously tracks mobility patterns to anticipate potential falls; Machine learning models utilize historical health data to provide proactive fall prevention alerts.
7. The multi-sensor fall detection and causality assessment system (100) as claimed in Claim 1, wherein ECG-derived PR interval analysis assesses vagal tone variations to identify potential neurological conditions contributing to falls.
8. The multi-sensor fall detection and causality assessment system (100) as claimed in Claim 1, wherein emergency alerts are automatically classified based on fall severity and transmitted to appropriate healthcare providers.
9. The multi-sensor fall detection and causality assessment system (100) as claimed in Claim 1, wherein the system cross-references previous falls, medical conditions, and medication side effects to improve personalized fall detection (300) accuracy.