A study of the influence of the sensor sampling frequency on the performance of wearable fall detectors

• Published in: Measurement : journal of the International Measurement Confederation Vol. 193; p. 110945
• Main Authors: Antonio Santoyo-Ramón, José, Casilari, Eduardo, Manuel Cano-García, José
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 01.04.2022; Elsevier Science Ltd
• Subjects: Accelerometer; Artificial neural networks; Convolutional Neural Network; Dataset; Deep Learning; Detectors; Fall detection; Fall Detection Systems; Human Activity Recognition; Inertial Sensors; Neural networks; Power consumption; Sampling; Sampling Rate; Sensors; Wearable computers; Wearable technology; Convolutional Neural Network; Dataset; Accelerometer; Sampling Rate; Inertial Sensors; Fall Detection Systems; Human Activity Recognition; Deep Learning
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2022.110945

•Impact of sampling frequency on wearable fall detection system (based on accelerometer signals) is normally neglected.•Sampling rate is normally arbitrarily chosen although it can heavily impact on the battery lifetime of the wearable.•The paper assesses the influence of the sampling rate on the effectiveness of a deep learning classifier (a convolutional neural network) directly fed with an observation window of accelerometry signals.•The study reveals that a frequency of 15–20 Hz may be enough for a proper operation of a wearable (specificity/ sensitivity higher than 95%).•Results are confirmed when the power spectrum of the signals is investigated and when the series are low pass filtered.•The systematic study is based on the massive analysis of 15 public datasets (related literature typically utilizes just 1 or 2 datasets). Last decade has witnessed a major research interest on wearable fall detection systems. Sampling rate in these detectors strongly affects the power consumption and required complexity of the employed wearables. This study investigates the effect of the sampling frequency on the efficacy of the detection process. For this purpose, we train a convolutional neural network to directly discriminate falls from conventional activities based on the raw acceleration signals captured by a transportable sensor. Then, we analyze the changes in the performance of this classifier when the sampling rate is progressively reduced. In contrast with previous studies, the detector is tested against a wide set of public repositories of benchmarking traces. The quality metrics achieved for the different frequencies and the analysis of the spectrum of the signals reveal that a sampling rate of 20 Hz can be enough to maximize the effectiveness of a fall detector.