Post-fall Detection Using ANN Based on Ranking Algorithms
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International Journal of Precision Engineering and Manufacturing https://doi.org/10.1007/s12541-020-00398-6
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Post‑fall Detection Using ANN Based on Ranking Algorithms Bummo Koo1 · Jongman Kim1 · Taehee Kim1 · Haneul Jung1 · Yejin Nam1 · Youngho Kim1 Received: 6 May 2020 / Revised: 15 July 2020 / Accepted: 27 July 2020 © Korean Society for Precision Engineering 2020
Abstract The purpose of this study was to develop an accurate and efficient algorithm for post-fall detection. Thirty healthy male subjects were recruited and asked to perform 23 movements, comprising 14 activities of daily living and nine fall motions. The algorithm was developed using the ANN toolbox provided with MATLAB and inertial measurement unit (IMU) data were used to distinguish between fall and non-fall cases. An IMU sensor was located at the center between the left and right anterior superior iliac spines. A total of 32 feature vectors were extracted from 3-axis acceleration and angular velocity signals. Based on the five different ranking algorithms (relief-F, T-score, correlation, Fisher score, and minimum redundancy maximum relevance) used, feature vector subsets comprising the feature vectors were created and subsequently evaluated. Accuracy was compared according to the number of feature vectors constituting the subset, which were based on rank-lists. The results showed that the subset comprising all the feature vectors showed the best accuracy (99.86%), but a similar accuracy could be obtained with a subset comprising fewer feature vectors. The T-score was found to be the most optimal among the five ranking algorithms. Furthermore, T-score with two feature vectors achieved an accuracy of 99.17%. The results of this study are expected to assist in the construction of subsets of feature vectors based on ranking algorithms for post-fall detection with high accuracy and less computational cost. Keywords Post-fall detection · IMU · Machine learning · ANN · Ranking algorithm
1 Introduction For people above 65 years of age, the rate of falls and fall related injuries increases from 28 to 42%, according to the World Health Organization [1]. Falls may have grave consequences; however, most of these consequences are not directly related to falls and are instead caused by the lack of * Youngho Kim [email protected] Bummo Koo [email protected] Jongman Kim [email protected] Taehee Kim [email protected] Haneul Jung [email protected] Yejin Nam [email protected] 1
Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea
timely assistance and treatment [2]. Even when the injuries are not too serious, the elderly often struggle to get back up unaided [3] and lying on the ground because of a fall for long periods of time can lead to dehydration, pressure sores, pneumonia, hypothermia, and even death [4]. Therefore, numerous studies have focused on the detection of falls and on the provision of timely help. There are two methods that are commonly used for fall det
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