Detecting Falls as Novelties in Acceleration Patterns Acquired with Smartphones

Abstract

Despite being a major public health problem, falls in the elderly cannot be detected efficiently yet. Many studies have used acceleration as the main input to discriminate between falls and activities of daily living (ADL). In recent years, there has been an increasing interest in using smartphones for fall detection. The most promising results have been obtained by supervised Machine Learning algorithms. However, a drawback of these approaches is that they rely on falls simulated by young or mature people, which might not represent every possible fall situation and might be different from older people's falls. Thus, we propose to tackle the problem of fall detection by applying a kind of novelty detection methods which rely only on true ADL. In this way, a fall is any abnormal movement with respect to ADL. A system based on these methods could easily adapt itself to new situations since new ADL could be recorded continuously and the system could be re-trained on the fly. The goal of this work is to explore the use of such novelty detectors by selecting one of them and by comparing it with a state-of-the-art traditional supervised method under different conditions. The data sets we have collected were recorded with smartphones. Ten volunteers simulated eight type of falls, whereas ADL were recorded while they carried the phone in their real life. Even though we have not collected data from the elderly, the data sets were suitable to check the adaptability of novelty detectors. They have been made publicly available to improve the reproducibility of our results. We have studied several novelty detection methods, selecting the nearest neighbour-based technique (NN) as the most suitable. Then, we have compared NN with the Support Vector Machine (SVM). In most situations a generic SVM outperformed an adapted NN.

UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

“…Finally, studies on this topic confirm the activities we selected are common in real-life [16,[29][30][31].…”

“…It allows measurements of acceleration in three perpendicular axes, and allows acceleration ranges from ±2g to ±16g and sampling rates from 1KHz to 32Hz. For the experiments presented in this paper, the sampling rate was about 50 Hz, which is commonly used in literature for activity recognition from data acquired through smartphones [15][16][17]. The accelerometer signal is for each time instant made of a triplet of numbers (x, y, z) that represents the accelerations along each of the 3 Cartesian axes.…”

A comparison of public datasets for acceleration-based fall detection

“…This could lead to a dramatic decrease in performance when using the detectors in a real-world context, resulting in the rejection of the technology by its potential users. The personalization and adaptation of the system are key aspect to overcome this problem [20].…”

“…In this regard, several authors have identified the need for having public datasets [19,20]. Some efforts have been performed in this direction since several datasets were made publicly available in the recent years: DLR [21] published in 2011, MobiFall [22] available in 2013 and tFall [20] uploaded in 2014 (the study of Fudickar et al [23] cites another public dataset but it seems that it cannot be downloaded currently).…”

“…Some efforts have been performed in this direction since several datasets were made publicly available in the recent years: DLR [21] published in 2011, MobiFall [22] available in 2013 and tFall [20] uploaded in 2014 (the study of Fudickar et al [23] cites another public dataset but it seems that it cannot be downloaded currently). Although these three datasets can be freely accessed, there is no study focused on comparing them.…”