Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> eHome <s> Within the project "eHome" a prototype of an assistive home system was developed, aiming to prolong the independent life of elderly people at home. Besides communication, e-access and safety relevant features, a core part of this system is an automatic fall detection, which utilizes floor-mounted accelerometers to gather body-sound signals that typically occur during a human fall. This approach targets to avoid acceptance, usability and reliability issues of available body-mounted fall detectors. The system was developed with focus on practical applicability, reliability and exploitability. The prototype was evaluated successfully in laboratory and during 507 days in real-life at homes of persons from the target group. During the laboratory trials a sensitivity of 87% and a specificity of 97.7% could be achieved for a defined fall scenario and across four tested floors. Further research is suggested to investigate floor dependencies of the fall detection performance. <s> BIB001 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> eHome <s> A fall monitor system is necessary to reduce the rate of fall fatalities in elderly people. As an accelerometer has been smaller and inexpensive, it has been becoming widely used in motion detection fields. This paper proposes the falling detection algorithm based on back propagation neural network to detect the fall of elderly people. In the experiment, a tri-axial accelerometer was attached to waists of five healthy and young people. In order to evaluate the performance of the fall detection, five young people were asked to simulate four daily-life activities and four falls; walking, jumping, flopping on bed, rising from bed, front fall, back fall, left fall and right fall. The experimental results show that the proposed algorithm can potentially distinguish the falling activities from the other daily-life activities. <s> BIB002
Werner et al., developed an ambient assisted living system (AAL) that thrives for giving old people the feeling of security in their homes. The prototype was created in 2011 and was named eHome BIB001 . The idea behind ehome is to provide a user's home with a set of unintrusive wireless sensors to gather his/her behavioral data when faced to a life-threatening situation such as a fall. The system uses floor-mounted accelerometers to collect vibration patterns of an individual that are sent wirelessly to a base station where data analysis and decision-making is accomplished. The fall detection process is based on the belief that distinctive impact vibrations propagate through the floor when a fall occurs. These vibrations are compared to a threshold in order to identify if a fall-related accident took place. When a fall is detected the user is asked if he/she is ok or not. If the user does not respond within a predefined period an alarm is triggered and relatives and care givers are notified. eHome uses at least three sensor boxes that are placed in the middle of the room's edges; the boxes are within an aggregated network. The Fast Fourier Transform is one of the techniques used for feature extraction. The initial experiments were performed in a lab environment and later on in real home settings. No real fall happened in the real trials, as a result no conclusion for this scenario was provided. The recall and specificity in the experiments were 87% and 97%, respectively. 6.1.7. Sengto et al. In 2012, Sengto et al. proposed a fall detection system algorithm based on a back propagation neural network (BPNN) BIB002 . The system utilizes a tri-axial accelerometer mounted on the user's waist in order to collect his/her acceleration data behavior. Human activities are divided in three groups: falling activities (forward, backward, right and left), slow motion activities (walking, getting up from bed, flopping), and sudden motion activities (running, jumping). An acceleration threshold is set to differentiate between slow motion activities and other activities. Sudden motion and falling activities have similar acceleration signal patterns. If the threshold is surpassed a BPNN will determine if a fall occurred. The overall recall of the detection algorithm was 96.25% while the specificity was 99.5%.