Abstract
Since fall is happening with increasing frequency, it has been a major public health problem in an aging society. There are considerable demands to distinguish fall down events of seniors with the characteristics of accurate detection and real-time alarm. However, some daily activities are erroneously signaled as falls and there are too many false alarms in actual application. In order to resolve this problem, this paper designs and implements a comprehensive fall detection framework on the basis of inertial posture sensors and surveillance cameras. In the proposed system framework, data sources representing behavior characteristics to indicate potential fall are derived from wearable triaxial accelerometers and monitoring videos of surveillance cameras. Moreover, the NB-IoT based communication mode is adopted to transmit wearable sensory data to the Internet for subsequent analysis. Furthermore, a Gradient Boosting Decision Tree (GBDT) classifier-based fall detection algorithm (GBDT-FD in short) with comprehensive data fusion of posture sensor and human video skeleton is proposed to improve detection accuracy. Experimental results verify the good performance of the proposed GBDT-FD algorithm compared to six kinds of existing fall detection algorithms, including SVM-based fall detection, NN-based fall detection, etc. Finally, we implement the proposed integrated systems including wearable posture sensors and monitoring software on the Cloud Server.
Highlights
An increased aging population in the world is forcing rapid rises in healthcare requirements [1]
Real-time acceleration data from posture sensors are transmitted by NB-IoT communication mode to the Cloud Server, and form a data collection named as Acceleration DataSet (ADS)
More than 6000 activities records are used to verify our Gradient Boosting Decision Tree (GBDT) based algorithm, 20% of them are used for training, and the rest 80% is used for testing
Summary
An increased aging population in the world is forcing rapid rises in healthcare requirements [1]. Sensory readings are processed and analyzed using a decision tree based Big Data model running on a Smart IoT Gateway [14] These wearable sensors have high sensitivity and good real-time characteristics, higher detection accuracy cannot typically be achieved due to the interference from diverse activities of hand or wrist. Vision-based fall detection can use relatively cheap cameras to quantify and judge various activities; it requires complex handling methods to construct a human body model and it is unsuitable for real-time detection mode. It is not very realistic to detect fall accidents with so many sensors To overcome these shortcomings, we use an ordinary camera and accelerometer as a data source in this paper so as to improve the practicability of the detection system.
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