FallAllD: An Open Dataset of Human Falls and Activities of Daily Living for Classical and Deep Learning Applications

In the last two decades, a wide variety of wearable fall detection systems have been proposed. Most of them were based on machine learning. While the reported results give the impression that the problem is almost solved, crucial questions raise about the representation capacity of the considered datasets and accordingly the reliability of performance evaluation. In this article, the limitations of a multitude of state-of-the-art fall detection datasets are discussed. Particularly, limitations related to the sensors that are used to capture the motion signals, the positions of these wearable sensors, the sampling frequency and the measurement range are discussed. Moreover, limitations concerning the simulation protocol e.g. the considered types of falls are discussed. A comprehensive data acquisition system and simulation protocol are proposed to overcome these limitations. Consequently, a large dataset, namely FallAllD, is proposed. It consists of 26 420 files collected using three data-loggers worn on the waist, wrist and neck of the subjects. Motion signals are captured using an accelerometer, gyroscope, magnetometer and barometer with efficient configurations that suit the potential applications. The performance of deep learning and classical learning-based algorithms is evaluated on the proposed dataset as well as some reference datasets. The results show significant performance differences when considering different datasets. The reasons underlying these differences are discussed and the advantages of FallAllD are highlighted. Moreover, challenges of acceleration-based fall detection are deeply analyzed. This analysis reveals the main reasons underlying false positives and false negatives. FallAllD could be used in fall detection, fall prevention and human activity recognition contexts.