Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> PerFallD <s> Falls are a major health risk that diminish the quality of life among elderly people. With the elderly population surging, especially with aging “baby boomers”, fall detection becomes increasingly important. However, existing commercial products and academic solutions struggle to achieve pervasive fall detection. In this paper, we propose utilizing mobile phones as a platform for pervasive fall detection system development. To our knowledge, we are the first to do so. We design a detection algorithm based on mobile phone platforms. We propose PerFallD, a pervasive fall detection system implemented on mobile phones. We implement a prototype system on the Android G1 phone and conduct experiments to evaluate our system. In particular, we compare PerFallD's performance with that of existing work and a commercial product. Experimental results show that PerFallD achieves strong detection performance and power efficiency. <s> BIB001 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> PerFallD <s> The injuries caused by falls are great threats to the elderly people. With the ability of communication and motion sensing, the mobile phone is an ideal platform to detect the occurrence of fall accidents and help the injured person receive first aid. However, the missed detection and false alarm of the monitoring software will cause annoyance to the users in real use. In this paper, we present a novel fall detection technique using features from a five-phase model which describes the state change of the user's motion during the fall. Experiment results validate the effectiveness of the algorithm and show that the features derived from the model as gravity-cross rate and non-primarily maximum and minimum points of the acceleration data are useful to improve the precision of the detection. Moreover, we implement the technique as uCare, an Android application that helps elderly people in fall prevention, detection and first aid seeking. <s> BIB002
This system BIB001 was one of the first, if not the first, smartphone android-based fall detection system. It was introduced by Dai et al., in 2010. PerFallD takes advantage of the sensors (e.g., accelerometer, gyroscope, etc.) and communication technologies embedded in a smartphone to not only detect falls, but also to send alerts automatically when a patient is not responsive within a pre-specified time after a fall. Its user interface is highly friendly, and no additional hardware is required. The acceleration and angular data are used to set thresholds that determine if a fall has occurred. People nowadays are used to carrying their cellphones everywhere, making PerfallD highly unobtrusive. Multiple experiments were performed placing the phone in different locations (chest, waist and thigh); different thresholds had to be set per location. The waist was found to be the best location with an average performance of 2.76% for FN and 8.7% for FP. 6.1.5. uCare uCare BIB002 is another fall detection system based on an android-based smartphone. It was developed by Shi et al., in 2012, and it integrates a support vector machine (SVM) (Section 2) in the classification stage. The proposed technique uses the acceleration data from the phone's accelerometer to detect a fall. The fall detection process is divided into five phases namely, normal, unstable, free fall, adjustment, and motionless. Normal refers to ADLs. Unstable is related with the moment prior the fall. Free fall is experimented when falling. Adjustment deals with the impact of the fall. Motionless is the period of time after the fall when the subject is not moving. An acceleration threshold is used to trigger the five-phases feature extraction method. A 16-elements vector is obtained as a result of the extraction method. This vector is fed into a SVM that is used to differentiate falls from ADLs. The data collection and experiments were conducted in a lab environment using a Google Nexus S phone and MATLAB was used to build the classifier. The acquired results were the following: recall 90% and precision 95.7%.