Daily activity recognition based on recurrent neural network using multi-modal signals

Abstract

Our aim is to develop a smartphone-based life-logging system. Human activity recognition (HAR) is one of the core techniques to realize it. Recent studies reported the effectiveness of feed-forward neural network (FF-NN) and recurrent neural network (RNN) as a classifier for HAR task. However, there are still unresolved problems in those studies: (1) a life-logging system using only a smartphone for recording device has not been developed, (2) only indoor activities have been utilized for evaluation, (3) insufficient investigations/evaluations of RNN. In this study, we address these unresolved problems as follows: (1) we build a prototype system for life-logging and conduct data recording experiment on this system to include both indoor and outdoor activities. The experimental results of HAR on this new dataset showed that RNN-based classifier was still effective. (2) From the results of a HAR experiment, it was demonstrated that a multi-layered Simple Recurrent Unit with a non-linear transform at the bottom layer and a highway-connection was the most effective. (3) We could grasp the reason for the improvement of RNN from FF-NN by observing the posterior probabilities over test data.

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

“…Initially, the idea of using temporal information was proposed in 1991 [178] to recognize a finger alphabet consisting of 42 symbols and in 1995 [179] to classify 66 different hand shapes with about 98% accuracy. Since then, the recurrent neural network (RNN) with time series as input has been widely applied to classify human activities or estimate hand gestures [180][181][182][183][184][185][186][187].…”

Temporal Convolutional Network with Wavelet Transform for Fall Detection

“…Since it was proposed in 1991 [37], RNN with time series as input has been widely used for human activity classification or gesture estimation [38][39][40][41][42][43][44]. Many researchers have carried out extensive work to improve the performance of RNN models in HAR [45][46][47], and Torti et al [48] propose an RNN system for fall detection suitable for a microcontroller embedded implementation, with an overall detection rate of 98%.…”

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

“…Initially, the idea of using temporal information was proposed in 1991 [141] to recognize a finger alphabet consisting of 42 symbols and in 1995 [199] to classify 66 different hand shapes with about 98% accuracy. Since then, the recurrent neural network (RNN) with time series as input has been widely applied to classify human activities or estimate hand gestures [37,43,90,124,189,190,194].…”