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

Improving the Performance of Fall Detection Systems through Walk Recognition

Social problems associated with falls of elderly citizens are becoming increasingly important because of the continuous growth of aging population. Automatic fall detection systems represent a possible answer to some of these problems, as they are useful to obtain help in case of serious injuries and to reduce the long-lie problem. Nevertheless, widespread adoption of these systems is strongly influenced by their usability and trustworthiness, which are at the moment not excellent. In fact, the user is forced to wear the device according to placement and orientation restrictions that depend on the considered fall-recognition technique. Also, the number of false alarms generated is too high to be acceptable in real world scenarios. This paper presents a technique, based on walk recognition, that increases significantly both usability and trustworthiness of a smartphone-based fall detection system. In particular, the proposed technique automatically and dynamically determines the orientation of the device, thus relieving the user from the burden of wearing the device with predefined orientation. Orientation is then used to infer posture and eliminate a large fraction of false alarms (98 %)

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

From single point of measurement to distributed sensing in long-term glacier monitoring

Glacial environment monitoring is a key task in understanding natural phenomena related to global warming. For the last 30 years, Automatic Weather Stations (AWSs) have been spreading among the meteorological and geophysical community, and are on the way to become a de facto standard to perform long-lasting unattended data acquisitions in single localized points of interest. Sensor Networks (SNs), on the other hand, promise the possibility to perform measurements with a higher spatial density and lower cost. Designing and developing a SN for glacial environment face particular challenges for embedded electronics and sensor systems, which is why SNs are still under research and development in this eld. This paper surveys the AWSs and SNs for glacial monitoring applications and compares their characteristics

Fall detection using a head-worn barometer

Falls are a significant health and social problem for older adults and their relatives. In this paper we study the use of a barometer placed at the user’s head (e.g., embedded in a pair of glasses) as a means to improve current wearable sensor-based fall detection methods. This approach proves useful to reliably detect falls even if the acceleration produced during the impact is relatively small. Prompt detection of a fall and/or an abnormal lying condition is key to minimize the negative effect on health

Deploying a Communicating Automatic Weather Station on an Alpine Glacier

The cost and effort of installing and maintaining an automatic weather station (AWS) on a glacier may be mitigated by the possibility of gathering sensor data in near real-time, and of controlling and programming the station remotely. In this paper we report our experience with upgrading an existing AWS, operating over an Italian glacier, from a mere datalogger into a networked sensing station. Design choices, energy constraints and power-aware programming of the station determined by harsh environment are discussed. Deployment operations and results are described. The upgraded AWS provides low-power connectivity from a remote location and is able to serve as a base station for a wireless sensor network working in the glacier

Impromptu crisis mapping to prioritize emergency response

To visualize post-emergency damage, a crisis-mapping system uses readily available semantic annotators, a machine-learning classifier to analyze relevant tweets, and interactive maps to rank extracted situational information. The system was validated against data from two recent disasters in Italy