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

Posture Recognition Using the Interdistances Between Wearable Devices

Recognition of user's postures and activities is particularly important, as it allows applications to customize their operations according to the current situation. The vast majority of available solutions are based on wearable devices equipped with accelerometers and gyroscopes. In this article, a different approach is explored: The posture of the user is inferred from the interdistances between the set of devices worn by the user. Interdistances are first measured by using ultra-wideband transceivers operating in two-way ranging mode and then provided as input to a classifier that estimates current posture. An experimental evaluation shows that the proposed method is effective (up to ∼98.2% accuracy), especially when using a personalized model. The method could be used to enhance the accuracy of activity recognition systems based on inertial sensors

Modularity-based approach for tracking communities in dynamic social networks

Community detection is a crucial task to unravel the intricate dynamics of online social networks. The emergence of these networks has dramatically increased the volume and speed of interactions among users, presenting researchers with unprecedented opportunities to explore and analyze the underlying structure of social communities. Despite a growing interest in tracking the evolution of groups of users in real-world social networks, the predominant focus of community detection efforts has been on communities within static networks. In this paper, we introduce a novel framework for tracking communities over time in a dynamic network, where a series of significant events is identified for each community. Our framework adopts a modularity-based strategy and does not require a predefined threshold, leading to a more accurate and robust tracking of dynamic communities. We validated the efficacy of our framework through extensive experiments on synthetic networks featuring embedded events. The results indicate that our framework can outperform the state-of-the-art methods. Furthermore, we utilized the proposed approach on a Twitter network comprising over 60,000 users and 5 million tweets throughout 2020, showcasing its potential in identifying dynamic communities in real-world scenarios. The proposed framework can be applied to different social networks and provides a valuable tool to gain deeper insights into the evolution of communities in dynamic social networks

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 %)

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 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

Detecting elderly behavior shift via smart devices and stigmergic receptive fields

Smart devices are increasingly used for health monitoring. We present a novel connectionist architecture to detect elderly behavior shift from data gathered by wearable or ambient sensing technology. Behavior shift is a pattern used in many applications: it may indicate initial signs of disease or deviations in performance. In the proposed architecture, the input samples are aggregated by functional structures called trails. The trailing process is inspired by stigmergy, an insects’ coordination mechanism, and is managed by computational units called Stigmergic Receptive Fields (SRFs), which provide a (dis-)similarity measure between sample streams. This paper presents the architectural view, and summarizes the achievements related to three application case studies, i.e., indoor mobility behavior, sleep behavior, and physical activity behavior

Detection of falls and gait anomalies using wearable sensors

In the last few decades, continuous monitoring of human movements has become possible thanks to the widespread adoption of wearable devices equipped with inertial sensors. More specifically, MEMS accelerometers have enabled the collection of motion data during the activities of daily living and in almost any environment. Pervasive and ubiquitous monitoring of users has opened up the way to innovative applications with the aim of improving health and well-being. One relevant example is represented by fall detection systems. Falls are a leading cause of injury and hospitalization among the elderly population. Since the elderly are often unable to call for help after falling, there has been increasing interest in reliable systems for the automatic detection of falls. Wearable sensors, like accelerometers and gyroscopes, offer a low-cost solution to the problem, characterized by fast and easy deployment. However, the adoption of this approach has been hindered by the high rate of false alarms as well as usability-related concerns. In this thesis, we propose novel techniques to improve the accuracy and usability of fall detection systems based on wearable devices. Our approach relies on a specific set of accelerometric features to discriminate normal activities from falls. To minimize the impact on everyday life, our method requires the use of a single device, which can be comfortably carried in a trouser pocket. Also, the proposed technique can be executed on the wearable sensor, thus reducing wireless transmissions and saving battery life. In addition to fall detection, we propose a method for capturing abnormal deviation in a user's gait pattern. Previous research has highlighted the importance of gait analysis to assess frailty and fall risk in the elderly. Gait changes, such as reduced stability or speed, have been also used as early indicators of cognitive impairment. Prompt detection of gait anomalies could thus enable early intervention to monitor degenerative diseases and prevent falls

An innovative approach to false alarm recognition in fall detection systems

Falls are a mayor cause of injury deaths and injury-related hospitalization among people older than 65 years. Even non-injurious falls can be really dangerous, as it has been shown that many elderly people lack the ability to stand up and remain on the ground for even longer than an hour. This is the so called "long-lie", and it has been proved to have devastating effects on health. At the same time, fall related admission of older adults are a significant burden to the health services world wide. Therefore, in the latest years there has been a great interest on fall detection systems by the healthcare industry. Many attempts to solve this problem using wearable devices has been already made: prevalent methods use a fixed accelerometer threshold to isolate falls from the "activities of daily living" (ADL). This approach does not succeed in distinguishing actual falls from certain fall-like activities such as sitting down or lying quickly, and causes frequent false alarms. In order to improve the detection accuracy, some researchers propose to combine linear acceleration obtained from accelerometers with gyroscope measurement of angular velocity. Unfortunately gyroscopes have a really negative impact on device battery lifetime. Body orientation has also been used to improve detection, but it is not very useful as there is no clear connection between posture and fall. In this work we present a novel method for false alarm recognition and filtering, which leads to a significantly improved level of detection accuracy. Our approach features low computational costs and real time response. Moreover, it requires only an accelerometer placed at user's waist, achieving a high degree of usability