Fall events are one of the main causes of injuries among the elderly. The purpose of this study has been to identify a computational framework for the real-time and automatic detection of the fall risk, allowing the fast adoption of properly intervention strategies, to reduce injuries and traumas due to the fall. A wearable, wireless and minimally invasive surface Electromyography (EMG)-based system has been used to measure four lower-limb muscles activities. Eleven young healthy subjects have simulated several fall events (through a movable platform) and normal Activities of Daily Living (ADLs) and their patterns have been analysed. Highly discriminative features extracted within the EMG signals for the pre impact fall evaluation have been explored and a threshold-based approach has been adopted, assuring the real-time functioning. The threshold level for each feature has been set to distinguish an instability condition from normal activities. The proposed system seems able to recognize all falls with an average lead-time of 840ms before the impact, in simulated and controlled fall conditions.

Falls are very dangerous events among elderly people. Several automatic fall detectors have been developed to reduce the time of the medical intervention, but they cannot avoid the injures due to the fall. In this paper a study about the feasibility of a wearable system for the detection of the fall risk is presented. The aim is to detect the fall before to the impact on the floor, allowing the intervention of an impact reduction system. An electromyography-based system has been chosen because it can recognize the risk of fall faster than other more commonly used inertial sensors based system. The logical framework implements the feature extraction procedure, demonstrating a higher discriminative power of Co-contraction Indices and Integrated EMG for which at least of 70% of specificity and sensitivity are achieved in the classification process.

The paper presents a wearable system able to evaluate real time the risk of fall in elderly people, promoting the fast adoption of properly intervention strategies for reducing injuries (e.g. by activating an impact reduction system). A wireless and minimally invasive surface Electromyography-based system (EMG) has been used to measure four lower limb muscles activities. This work deals with the identification of highly discriminative features extracted from the EMG signals for the automatic detection of people instability. The framework prototype uses a threshold-based approach assuring real time functioning and permitting the detection of a typical imbalance condition about 200ms after the stimulus perturbation, in simulated and controlled fall conditions.

The paper presents a preliminary study on the feasibility of a pre-impact fall detector, able to evaluate the risk of fall real-time, allowing the fast adoption of properly strategies of intervention for reducing injuries (e.g. By activating an impact reduction system). A wearable, wireless and minimally invasive surface Electromyography-based system (EMG) has been used to measure four lower-limb muscles activities. This work deals with the identification of highly discriminative features extracted within the EMG signals for the automatic detection of people instability. The framework prototype uses a threshold-based approach assuring real-time functioning and allowing the detection of a typical imbalance condition about 200ms after the stimulus perturbation, in simulated and controlled fall conditions.

This paper presents a heterogeneous sensor platform for the detection of anomalies in circadian rhythm. Three detectors with different sensing principles are considered: a 3D time-of-flight camera, a MEMS wearable wireless accelerometer and a Ultra-wideband radar. Starting from human postural information obtained by each detector, a simulator of activities and related postures has been designed and implemented within this work. The use of a simulator is motivated by the lack of datasets containing long-term data for the analyzed context. The simulator is able to generate posture sequences calibrated on real experiments performed by each detector involved in the platform. Finally, a reasoner layer infers knowledge by using a suitable activity recognition module. Moreover, with an unsupervised clustering technique, the reasoner is able to detect specific circadian anomalies, thereby providing a tool for clinical evaluations. Experimental evaluation shows the effectiveness of the implemented solution, especially analyzing the performances related to the detection of anomalies varying sensing technology.

Anomalies in the circadian rhythm may cause psychological or neurological disorders, mainly in elderly people. Early detection of anomalies in circadian rhythm could be useful for the prevention of such problems. This work describes a multi-sensor platform for anomalies detection in circadian rhythm. Three detectors with different sensing principles are considered: a Time-Of-Flight camera, a MEMS wearable wireless accelerometer and an Ultra-Wideband radar. The inputs of the platform are sequences of human postures, even simulated, extensively used both for analysis of Activities of Daily Living and human behavior understanding. A postures simulator, calibrated on real experiments performed by each detector involved in the platform, has been implemented in order to compensate the lack of wide datasets containing long-term data for the analyzed context. Finally, a reasoner layer infers knowledge by using a suitable activity recognition module; by means of an unsupervised clustering technique, the reasoner is able to detect specific circadian anomalies, providing a tool for clinical evaluations. Experimental evaluation shows the effectiveness of the implemented solution and the ability to detect circadian anomalies at varying sensing technology.

This paper presents an open platform for continuous monitoring of clinical signs through a smart and noninvasive wearable device. In order to accomplish a communication in proximity, the Near Field Communication wireless technology is used, providing a fast link between the device and the host, avoiding the pairing (as typically occurs for Bluetooth protocol) and limiting the power consumption. The Arduino ecosystem has been used for prototyping since it allows an easy and open integration of ad-hoc functionalities. The first prototype of the platform has been customized for human body temperature measurement, assuring a lifetime of the battery for at least 2 months. Moreover, the acquisition and related transmission of other kind of clinical signs could be easily implemented, making the platform cost-effective in mobile scenarios.

This work describes a heterogeneous sensor platform for elderly people useful in Ambient Assisted Living context for sleep disorder evaluation. The platform integrates hardware (ambient and wearable sensors), as well as software components (data simulation tool, reasoning). Three sensors with different sensing principles are considered: a Time-Of-Flight camera, a MEMS wearable wireless accelerometer and an Ultra-Wideband radar. The inputs of the platform are the postural information, even simulated, in common to all involved sensors (i.e., Standing , Bending, Sitting, Lying down). Since they are extensively used both for analysis of Activities of Daily Living and human behaviour understanding. A posture simulator, calibrated on real experiments performed by each sensor involved in the platform, has been implemented in order to compensate the lack of wide datasets containing long-term data. Moreover, the platform integrates a reasoning layer for automatic sleep disorder evaluation by using an unsupervised learning technique. The effectiveness of the platform was demonstrated by preliminary results, exhibiting high accuracy in sleep disorder evaluation using the three aforementioned sensors.

Falling is one of the main causes of trauma, disability, and death among older people. Inertial sensors-based devices are able to detect falls in controlled environments. Often this kind of solution presents poor performances in real conditions. The aim of this work is the development of a computationally low-cost algorithm for feature extraction and the implementation of a machine-learning scheme for people fall detection, by using a triaxial MEMS wearable wireless accelerometer. The proposed approach allows to generalize the detection of fall events in several practical conditions. It appears invariant to the age, weight, height of people, and to the relative positioning area (even in the upper part of the waist), overcoming the drawbacks of well-known threshold-based approaches in which several parameters need to be manually estimated according to the specific features of the end user. In order to limit the workload, the specific study on posture analysis has been avoided, and a polynomial kernel function is used while maintaining high performances in terms of specificity and sensitivity. The supervised clustering step is achieved by implementing an one-class support vector machine classifier in a stand-alone PC. © 2013 Gabriele Rescio et al.

Falls are the leading cause of disability and death among the elderly. Over the years, several inertial-based wearable devices for automatic fall and pre-fall detection have been devised. Under controlled condition, these systems show a high performance for unbalance detection (up to 100% of specificity and sensitivity), however the mean lead time before the impact is about 200-400 ms. Although this period of time is enough to active an impact reduction system (i.e wearable airbag) to minimize injury, it is necessary to increase it so as to improve the system efficiency and reliability. A user's muscle behavior analysis could be more strategic than that of a kinematic evaluation one, permitting a rapid recognition of an imbalance event. This also holds true for several research studies on muscles response during a state of imbalance, whereas a limit number of them deal with the development of wearable electromyography (EMG)-based systems for human imbalance detection, suitable for predicting a lack of balance in real time. With respects these limitations, the main purpose of this work has been the development of a low computational cost expert system for real time and automatic fall risk detection. The analysis of this is achieved through lower limb muscles behavior monitoring. A Machine Learning scheme has been chosen in order to overcome the well-known drawbacks of threshold approaches widely used in pre-fall systems, in which the algorithm parameters have to be set according to the users' specific physical characteristics. Ten kinds of time-domain features, commonly used in the analysis of the lower-limb muscle activity, have been investigated. With a view to reducing the processing complexity, the Markov Random Field (MRF) based Fisher-Markov feature selector was tested. It showed a high degree of accuracy in the EMG-based features selection for lack of balance detection. The Co-Contraction Index, Integrated EMG and Willison Amplitude features have been also considered. The supervised classification phase has been obtained through a low computational cost and a high classification accuracy level Linear Discriminant Analysis. The developed system shows high performance in terms of sensitivity and specificity (about 90%) in controlled conditions, with a mean lead-time before the impact of about 775 ms. Therefore, the feasibility of a quick and wearable surface EMG-based unbalance detection system, by using Machine Learning methodology, has been demonstrated. The system may recognize a fall event during the initial phase, increasing the decision making time and minimizing the incorrect and inappropriate activations of the protection system, in real life scenario.

Fall events can cause trauma, disability and death among older people. Accelerometer-based devices are able to detect falls in controlled environments. The paper presents a computationally low-power approach for feature extraction and supervised clustering for people fall detection by using a 3-axial MEMS wearable accelerometer, managed by an stand-alone PC through ZigBee connection. The paper extends a previous work in which fall events were detected according to a threshold-based scheme. The proposed approach allows to generalize the detection of falls in several practical conditions, after a short period of calibration. The clustering scheme appears invariant to age, weight, height of people and relative positioning area (even in the upper part of the waist), overcoming the drawbacks of well-known threshold-based approaches in which several parameters need to be manually estimated, according to the specific features of the end-user. In order to limit the workload, the specific study on posture analysis has been avoided and a polynomial kernel function is used while maintaining high performance in terms of specificity and sensitivity. The supervised clustering step is achieved by implementing an One- Class Support Vector Machine classifier. © 2013 IEEE.

Falling down events can cause trauma, disability and death among older people. Accelerometer-based devices are able to detect falls in controlled environments. This kind of solution often presents poor performance in real conditions. The aim of this work is the development of a computationally low-cost algorithm for feature extraction and the implementation of a Machine Learning scheme for people fall detection, by using a tri-axial MEMS wearable wireless accelerometer. The proposed approach allows to generalize the detection of fall events in several practical conditions. It appears invariant to the age, weight, height of people and to the relative positioning area (even in the upper part of the waist), overcoming the drawbacks of well-known threshold-based approaches in which several parameters need to be manually estimated according to the specific features of the end-user. In order to limit the workload, the specific study on posture analysis has been avoided and a polynomial kernel function is used while maintaining high performance in terms of specificity and sensitivity. The supervised clustering step is achieved by implementing an One-Class Support Vector Machine classifier in a stand-alone PC. © 2013 IEEE.

The aim of this work is the development of a computationally low-cost scheme for feature extraction and the implementation of an One-class Support Vector Machine classifier for people fall detection, by using a tri-axial MEMS wearable wireless accelerometer, managed by a stand-alone PC through ZigBee connection. The proposed approach allows the generalization of the detection of fall events in several practical conditions after a short period of calibration. The approach appears invariant to age, weight, people's height and the relative positioning area (even in the upper part of the waist) This overcomes the drawbacks of well-known threshold-based approaches in which several parameters need to be manually estimated according to the specific features of the end-user. In order to limit the workload, the specific study on posture analysis has been avoided and a polynomial kernel function is used, while maintaining high performances in terms of specificity and sensitivity. © 2013 IEEE.