Recognition of false alarms in fall detection systems

Falls are a major cause of hospitalization and injury-related deaths among the elderly population. The detrimental effects of falls, as well as the negative impact on health services costs, have led to a great interest on fall detection systems by the health-care industry. The most promising approaches are those based on a wearable device that monitors the movements of the patient, recognizes a fall and triggers an alarm. Unfortunately such techniques suffer from the problem of false alarms: some activities of daily living are erroneously reported as falls, thus reducing the confidence of the user. This paper presents a novel approach for improving the detection accuracy which is based on the idea of identifying specific movement patterns into the acceleration data. Using a single accelerometer, our system can recognize these patterns and use them to distinguish activities of daily living from real falls; thus the number of false alarms is reduced

Fall detection using ultra-wideband positioning

Falls are a major health problem in our aging society. Fall detection systems are aimed at automatically sending an alarm in case of falls. Unfortunately most of the systems currently available, which use accelerometric sensors, are characterized by a relatively large number of false alarms. In fact, many activities of daily living may produce fall-like acceleration signals. We propose a method that uses ultra-wideband positioning to track the movements of the user and detect falls. Preliminary results show that the approach is reliable in detecting falls and simple postures

On the design of Remote Health Monitoring System

With improvement in technology and miniaturization of sensors, there have been attempts to utilize the new technology in various areas to improve the quality of human life. One main area of research that has seen adoption of the technology is the healthcare sector. The people in need of healthcare services find it very expensive, this is particularly true in developing countries. With improvement in technology previously expensive hospital equipment have been redesigned using current technology. The developments have seen a trend known as remote healthcare or previously known as Telemedicine. As a result, this paper is an attempt to solve a healthcare problem facing the society. The main objective of the paper is to design a remote healthcare system. It is comprised of three main parts. The first part being detection of a fall, second being detection of electrocardiogram commonly referred to as ECG or EKG( heartbeat detection) and the last part is providing the detected data for remote viewing. Remote viewing of the data enables a doctor or health specialist to monitor a patient’s health progress away from hospital premises

Development of a Wearable-Sensor-Based Fall Detection System

Fall detection is a major challenge in the public healthcare domain, especially for the elderly as the decline of their physical fitness, and timely and reliable surveillance is necessary to mitigate the negative effects of falls. This paper develops a novel fall detection system based on a wearable device. The system monitors the movements of human body, recognizes a fall from normal daily activities by an effective quaternion algorithm, and automatically sends request for help to the caregivers with the patient’s location

Usability study of a wireless monitoring system among Alzheimer's Disease elderly population

Healthcare technologies are slowly entering into our daily lives, replacing old devices and techniques with newer intelligent ones. Although they are meant to help people, the reaction and willingness to use such new devices by the people can be unexpected, especially among the elderly. We conducted a usability study of a fall monitoring system in a long-term nursing home. The subjects were the elderly with advanced Alzheimer’s disease. The study presented here highlights some of the challenges faced in the use of wearable devices and the lessons learned. The results gave us useful insights, leading to ergonomics and aesthetics modifications to our wearable systems that significantly improved their usability and acceptance. New evaluating metrics were designed for the performance evaluation of usability and acceptability

An enhanced fall detection system for elderly person monitoring using consumer home networks

Various fall-detection solutions have been previously proposed to create a reliable surveillance system for elderly people with high requirements on accuracy, sensitivity and specificity. In this paper, an enhanced fall detection system is proposed for elderly person monitoring that is based on smart sensors worn on the body and operating through consumer home networks. With treble thresholds, accidental falls can be detected in the home healthcare environment. By utilizing information gathered from an accelerometer, cardiotachometer and smart sensors, the impacts of falls can be logged and distinguished from normal daily activities. The proposed system has been deployed in a prototype system as detailed in this paper. From a test group of 30 healthy participants, it was found that the proposed fall detection system can achieve a high detection accuracy of 97.5%, while the sensitivity and specificity are 96.8% and 98.1% respectively. Therefore, this system can reliably be developed and deployed into a consumer product for use as an elderly person monitoring device with high accuracy and a low false positive rate

Combating Alarm Fatigue: The Quest for More Accurate and Safer Clinical Monitoring Equipment

As the demand for health-care services continues to increase, clinically efficient and cost-effective patient monitoring takes on a critically important role. Key considerations inherent to this area of concern include patient safety, reliability, ease of use, and cost containment. Unfortunately, even the most modern patient monitoring systems carry significant drawbacks that limit their effectiveness and/or applicability. Major opportunities for improvement in both equipment design and monitor utilization have been identified, including the presence of excessive false and nuisance alarms. When poorly optimized, clinical alarm activity can affect patient safety and may have a negative impact on care providers, leading to inappropriate alarm response time due to the so-called alarm fatigue (AF). Ultimately, consequences of AF include missed alerts of clinical significance, with substantial risk for patient harm and potentially fatal outcomes. Targeted quality improvement initiatives and staff training, as well as the proactive incorporation of technological improvements, are the best approaches to address key barriers to the optimal utilization of clinical alarms, AF reduction, better patient care, and improved provider job satisfaction

Reliable and secure body fall detection algorithm in a wireless mesh network

Falls in elderly is one of the most serious causes of severe injury. Lack in immediate medical help makes these injuries life threatening. An automatic fall detection system, presented in this research, would help reduce the arrival time of medical attention, reduce mortality rate and promote independent living. Therefore, the algorithm finds its application in the medical field, specifically in nursing homes. The system designed and presented in this research is not only capable of detecting human falls but also distinguishing them from routine fall-like activities. Falls are detected with the help of a small wearable embedded device, i.e. Texas Instruments\u27 eZ430 Chronos watch which is wireless development kit. The watch operates at an RF frequency of 915MHz to communicate with each other in a wireless network. The wearable wrist watch is programmable and has an in-built accelerometer sensor and microcontroller circuitry. The accelerometer sensor is motion sensitive and measures the acceleration due to gravity. Whenever a fall is detected the watch sends a signal to the neighboring watch, which is always in the monitoring mode. Signal transmission and reception between these devices is via wireless communication, where every node is a sensor forwarding the signal to the next node. A wireless mesh network helps in quick transmission of signals thereby alerting the authorities. In order to differentiate between body fall and Activities of Daily Life, various body motions and gestures have been studied and presented. The features of a real fall and that of normal human motions are extracted and analyzed from the data obtained by volunteers who participated in the research. Evaluation of results led to setting forth threshold values for parameters like acceleration, change in co-ordinate axes and angle of orientation. Over-passing the threshold raises a fall alarm to bring to the attention of the hospital authority

Analysis of Android Device-Based Solutions for Fall Detection

Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources) to fall detection solutions.Ministerio de Economía y Competitividad TEC2013-42711-