Wearable Pre-Impact Fall Detection System Based on 3D Accelerometer and Subject’s Height

Abstract

This study presents a low-power wearable system able to predict a fall by detecting a pre-impact condition, performed through a simple analysis of motion data (acceleration) and height of the subject. The system can detect a fall in all directions with an average consumption of 5.91 mA; i.e., it can monitor the activity of daily living (ADL), whether or not a fall occurs. The entire detection system uses a single wearable tri-axis accelerometer placed on the waist for the comfort of the wearer during a long-term application. The algorithm is based on the following hypothesis: "A region defined as balanced boundary circle, based on the user's height, is characterized by the fact the chance that an actual fall happening is minimal. When an activity is classified outside this circle, an acceleration analysis is performed to determine an impending fall condition". Our threshold-based algorithm was validated experimentally, first with 9 young healthy volunteers performing both normal ADL and fall activities and then using 10 ADL and 5 falls from public SisFall dataset. The results show that falls could be detected with an average lead-time of 259 ms before the impact occurs, with minimal false alarms (97.7% specificity) and a sensitivity of 92.6%. This is a good lead-time achieved thus far in pre-impact fall detection, permitting the integration of our detection system in a wearable inflatable airbag for hip protection.

An Integrated Sensing and Communication System for Fall Detection and Recognition Using Ultrawideband Signals

“…Table 6 compares the proposed approach with existing state-of-the-art works, which can be broadly categorized into wearable [14], [59], [60], [62]- [64] and non-wearable [23], [24], [26]- [29], [61], [65]- [68] methods for fall detection.…”

Analyzing and Comparing Deep Learning Models on an ARM 32 Bits Microcontroller for Pre-Impact Fall Detection

“…Data were recorded using a specific circuitry integrating an nRF52832 System-on-Chip (SoC) from Nordic Semiconductor and a 3axis accelerometer LIS3DH from STMicroelectronics at 100 Hz. The system description can be found in [5]. We recorded four types of falls, namely forward, backward, lateral right and lateral left, and three ADL, i.e.…”

“…Each data point within each series was labeled based on the type of movement (fall or ADL). Each file contains 8 columns, consisting of: The tiltAngle (in rad) is calculated using Equation 1 [5] :…”

“…One major hurdle to the deployment of wearable sensors on elderly is the accuracy of pre-impact fall detection system, often generating false alarms,i.e., alerting fall during activities of daily living (ADL). Thus, most of recent researches have focused on reducing false alarms and improving accuracy of the preimpact fall detection system [2]- [4] using either thresholdbased algorithm [5]- [18], conventional machine learning (ML) [15], [19], or deep learning approaches [20]- [36]. Thresholdbased algorithms have demonstrated good performances to distinguish pre-impact falls from ADL: sensitivities are in the range of 92% -99%, specificities are in the range of 95% -98% with an average lead time (time interval between fall detection and body-ground impact occurrences) in the range of 250 -400 ms. Two interesting papers present exceptional results with a sensitivity and specificity > 97% [17], [18] but the algorithms were evaluated on a very small sample of people.…”

A Wearable Micro-Electromechanical System Inertial Sensor System for Fall Behaviour Detection Based on a Multi-Level Threshold Algorithm

“…When the acquired feature signal exceeds the threshold value, the occurrence of a fall will be determined. The features include acceleration, 20 angular velocity, 21 position, 22 pressure 23 and height, 24 etc. The threshold method has been gradually favored in wearable fall detection systems because of its relatively small computation effort.…”