SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning

Abstract

This paper presents SmartFall, an Android app that uses accelerometer data collected from a commodity-based smartwatch Internet of Things (IoT) device to detect falls. The smartwatch is paired with a smartphone that runs the SmartFall application, which performs the computation necessary for the prediction of falls in real time without incurring latency in communicating with a cloud server, while also preserving data privacy. We experimented with both traditional (Support Vector Machine and Naive Bayes) and non-traditional (Deep Learning) machine learning algorithms for the creation of fall detection models using three different fall datasets (Smartwatch, Notch, Farseeing). Our results show that a Deep Learning model for fall detection generally outperforms more traditional models across the three datasets. This is attributed to the Deep Learning model’s ability to automatically learn subtle features from the raw accelerometer data that are not available to Naive Bayes and Support Vector Machine, which are restricted to learning from a small set of extracted features manually specified. Furthermore, the Deep Learning model exhibits a better ability to generalize to new users when predicting falls, an important quality of any model that is to be successful in the real world. We also present a three-layer open IoT system architecture used in SmartFall, which can be easily adapted for the collection and analysis of other sensor data modalities (e.g., heart rate, skin temperature, walking patterns) that enables remote monitoring of a subject’s wellbeing.

Deep Recurrent Neural Networks for Edge Monitoring of Personal Risk and Warning Situations

“…System. Different deep learning methods have been successfully used for fall detection [2,[25][26][27][28]. Analyzing those systems, we see that all these methods rely either on models with a huge number of parameters or on remote communication.…”

Privacy-Enhancing Fall Detection from Remote Sensor Data Using Multi-Party Computation

“…This may be unimodal input, i.e. only accelerometer [5,14,25,31,47]; multi-modal, e.g. accelerometer and gyroscope together [38]; or aggregating measurements from multiple sensors in a body-area network [27].…”

“…We now present error rates achieved by the models used in our MPCbased implementations of SmartFall [31] using the standard and [31] provide a publicly-available dataset comprising labelled IMU accelerometer measurements. These correspond to binary fall events from seven participants between 21-55 years old wearing a Microsoft Band 2 smartwatch.…”

“…Fall detection systems are concerned with automatically detecting the occurrence of falls within the homes of elderly and disabled users, with the aim of minimising response times to potential injuries and facilitating independent living [33]. These systems involve the collection of data, such as accelerometer data from a body-worn device [1,14,25,27,31,38,47], video imagery from cameras in the home [15,24,30,48], and proximity sensors in ceilings [43]. This data is typically classified with respect to a dataset of known fall and non-fall data using machine learning.…”

“…However, these still reveal significant information about users, such as their presence. Moreover, they do not generalise to the significant proportion of fall detection proposals using time-series IMU measurements from personal devices, such as smartphones and smartwatches [1,5,31,38,45]. A promising solution is multi-party computation (MPC), where measurements are split using a linear secret sharing scheme (LSSS) and distributed to mutually distrusting parties.…”