Cluster-Analysis-Based User-Adaptive Fall Detection Using Fusion of Heart Rate Sensor and Accelerometer in a Wearable Device

Abstract

This paper proposes an automatic fall detector in a wearable device that can reduce risks by detecting falls and promptly alerting caregivers. For this purpose, we propose cluster-analysis-based useradaptive fall detection using a fusion of heart rate sensor and accelerometer. The objectives of the proposed fall detector are to have high accuracy with a low-complexity model regardless of diverse conditions. To meet the objectives, we propose the best 13-dimensional feature subset by using feature selection. In addition, we verify the performance increment of combining a heart rate sensor with an accelerometer and the effectiveness of the cluster-analysis-based anomaly detection. We also show the effectiveness of the useradaptive method when using both heart rate and acceleration signals that were hardly covered in other papers. Finally, we prove that the performance of the proposed fall detector achieves is better than that of recent useradaptive and user-independent approaches. This study is the first attempt to demonstrate the merits of the user-adaptive approach using a combination of heart rate and acceleration signals to detect falls. Moreover, this paper also contributes to fall detection area by providing the data we collected.

Triaxial Accelerometer-Based Falls and Activities of Daily Life Detection Using Machine Learning

“…The performance of ANNs was also examined by Tahir et al [ 33 ] in combination with features extracted using Convolutional Neural Networks (CNNs) from the accelerometer data acquired by a pelvis-strapped accelerometer, reporting a classification accuracy of 92.23%. Nho et al [ 34 ] proposed the fusion of heart rate data and accelerometer data acquired from a wrist-strapped sensor for the task of fall detection. They reported a 92.22% fall detection accuracy using the fusion of accelerometer and heart rate-based features and Gaussian Mixture Models (GMMs) for clustering.…”

Vision-Based Fall Detection Using Dense Block With Multi-Channel Convolutional Fusion Strategy

“…Yu et al modeled acceleration data in hidden Markov model to detect falls automatically using a single wearable device [8]. Nho et al proposed a cluster-analysisbased user-adaptive fall detection method using a fusion of heart rate sensor and accelerometer [9]. In [10], a verticalvelocity-based pre-impacted fall detection method using a wearable inertial sensor was introduced to address near-fall issues.…”

Towards an Accelerometer-Based Elderly Fall Detection System Using Cross-Disciplinary Time Series Features

“…Among the recent researches, wearable sensor-based fall detection has become popular [14]- [22]. Nho et al [14] in proposed an adaptive fall detection approach, where a fusion of heart rate sensor and an accelerometer was used. Authors used a 13-dimensional feature subset that was reduced through a filter and wrapper feature selection method.…”