Human Action Recognition Using Deep Learning Methods on Limited Sensory Data

Abstract

In recent years, due to the widespread usage of various sensors action recognition is becoming more popular in many fields such as person surveillance, human-robot interaction etc. In this study, we aimed to develop an action recognition system by using only limited accelerometer and gyroscope data. Several deep learning methods like Convolutional Neural Network(CNN), Long-Short Term Memory (LSTM) with classical machine learning algorithms and their combinations were implemented and a performance analysis was carried out. Data balancing and data augmentation methods were applied and accuracy rates were increased noticeably. We achieved new stateof-the-art result on the UCI HAR dataset by 97.4% accuracy rate with using 3 layer LSTM model. Also, we implemented same model on collected dataset (ETEXWELD) and 99.0% accuracy rate was obtained which means a solid contribution. Moreover, the performance analysis is not only based on accuracy results, but also includes precision, recall and f1-score metrics. Additionally, a real-time application was developed by using 3 layer LSTM network for evaluating how the best model classifies activities robustly.

Design of a Wearable Wireless Multi-Sensor Monitoring System and Application for Activity Recognition Using Deep Learning

“…There are many studies on wearable wireless network systems for activity monitoring [13], [43], [44]. These are mostly based on Bluetooth and Zigbee technology due to low-power consumption.…”

“…The sensor nodes transmit the collected 3-axis accelerometer, walking speed, and heartbeat information to a smartphone [12]. Tufek et al created WSN to record acceleration and gyroscope information on the human body using Zigbee technology [13]. Hossain et al created a network using LoraWAN technology.…”

Human Daily Activity Recognition Performed Using Wearable Inertial Sensors Combined With Deep Learning Algorithms

“…After 10 repetitions, the average accuracy of the open database was 95.08%, and the mean accuracy of the database we recorded was 87.88%. [20] 93.70% LSTM-CNN [28] 95.78% Bidir-LSTM [31] 93.79% EHARS [32] 93.92% CNN-LSTM [33] 92.13% CNN-LSTM [34] 93.40% Ours 95.99%…”

“…The Table 5 lists the accuracies of several researches by using UCI open data set, we can find the proposed algorithm is better than other researches. Reference [20] used a threelayer LSTM model. Although reference [28] has an accuracy similar to ours, like references [33] and [34] it did not use only CNN but also LSTM, and thus the complexity of the algorithm was increased.…”

“…They used a three-layer long short-term memory (LSTM) model that reached 93.7% accuracy with the UCI HAR database. With additional data, the accuracy reached 97.4% [20]. In the activity recognition method of Sukor et al, who used acceleration sensors in smartphones, data were collected by adopting the acceleration of the open dataset using the data as the original input signal.…”