A Falling Detection System with wireless sensor for the Elderly People Based on Ergnomics

Abstract

Fall detection is an important problem in the application

Abnormal Gait Behavior Detection for Elderly Based on Enhanced Wigner-Ville Analysis and Cloud Incremental SVM Learning

“…In the detection processes, it turns out that the binary accuracy recognition rate can be detected nearly 100% for daily life activities. The results are similar with the method proposed in [12,17]. This is because only Booleans of falling down or not as binary results need to be checked out to determine if it is needed to trigger alarm.…”

“…In abnormal behavior detection or falling detection process, for safety reasons [10][11][12][13][14][15][16][17][18], the training data set is usually based on an attempt by young people to imitate those behaviors. Assumption of imitated gait in young volunteers replicating actual gait and falling of the elderly can make the recognition result less accurate in real applications.…”

“…(2) Wearable devices are used for motion data collection and analysis. In [10][11][12][13][14][15][16][17][18] gravity acceleration sensors or gyrometers were explored for motion detection. In [10] the falling detection algorithm was introduced based on critical sensor thresholds by a wearable sensor system composed by a 3-axis accelerometer and gyroscope.…”

“…However it achieved only a simple binary classification of falling or not falling. Ye et al [17] presented a wireless sensor architecture based human falling detection system using 3-axis acceleration sensor to measure and collect the elderly people activities acceleration and transfer data by zigbee-3G network to remote medical monitoring platform. However, only one-class SVM classification algorithm together with a threshold to achieve judgment needed to be further improved in terms of original data classification.…”

Development of an Assessment Method of Forearm Pronation/Supination Motor Function based on Mobile Phone Accelerometer Data for an Early Diagnosis of Parkinson’s Disease

“…In particular, sensors such as accelerometers and gyroscopes built into smartphones can collect information regarding the patient's movements; the sensors can measure muscular displacement, velocity, angular velocity, acceleration, etc. [1][2][3][4][5][6][7][8]. Convenient technical methods to record data on motor symptoms related to PD can improve the quality and efficiency of proper medical treatment and care [9][10][11][12][13].…”