CNN based approach for activity recognition using a wrist-worn accelerometer

Abstract

In recent years, significant advancements have taken place in human activity recognition using various machine learning approaches. However, feature engineering have dominated conventional methods involving the difficult process of optimal feature selection. This problem has been mitigated by using a novel methodology based on deep learning framework which automatically extracts the useful features and reduces the computational cost. As a proof of concept, we have attempted to design a generalized model for recognition of three fundamental movements of the human forearm performed in daily life where data is collected from four different subjects using a single wrist worn accelerometer sensor. The validation of the proposed model is done with different pre-processing and noisy data condition which is evaluated using three possible methods. The results show that our proposed methodology achieves an average recognition rate of 99.8% as opposed to conventional methods based on K-means clustering, linear discriminant analysis and support vector machine.

Deep learning for sensor-based activity recognition: A survey

“…Sensor Modality Deep Model Application Dataset (Almaslukh et al, 2017) Body-worn SAE ADL D03 (Alsheikh et al, 2016) Body-worn RBM ADL, factory, Parkinson D02, D06, D14 Body-worn, ambiemt RBM Gesture, ADL, transportation Self, D01 (Chen and Xue, 2015) Body-worn CNN ADL Self (Chen et al, 2016b) Body-worn CNN ADL D06 (Cheng and Scotland, 2017) Body-worn DNN Parkinson Self (Edel and Köppe, 2016) Body-worn RNN ADL D01, D04, Self (Fang and Hu, 2014) Object, ambient DBN ADL Self (Gjoreski et al, 2016) Body-worn CNN ADL Self, D01 (Guan and Ploetz, 2017) Body-worn, object, ambient RNN ADL, smart home D01, D02, D04 (Ha et al, 2015) Body-worn CNN Factory, health D02, D13 (Ha and Choi, 2016) Body-worn CNN ADL, health D13 (Hammerla et al, 2015) Body-worn RBM Parkinson Self (Hammerla et al, 2016) Body-worn, object, ambient DNN, CNN, RNN ADL, smart home, gait D01, D04, D14 (Hannink et al, 2017) Body-worn CNN Gait Self (Hayashi et al, 2015) Body-worn, ambient RBM ADL, smart home D16 (Inoue et al, 2016) Body-worn RNN ADL D16 (Jiang and Yin, 2015) Body-worn CNN ADL D03, D05, D11 (Khan et al, 2017) Ambient CNN Respiration Self (Kim and Toomajian, 2016) Ambient CNN Hand gesture Self (Kim and Li, 2017) Body-worn CNN ADL Self Body-worn, ambient RBM ADL, emotion Self Ambient RBM ADL Self (Lee et al, 2017) Body-worn CNN ADL Self (Li et al, 2016a) Object RBM Patient resuscitation Self (Li et al, 2016b) Object CNN Patient resuscitation Self (Li et al, 2014) Body-worn SAE ADL D03 Body-worn CNN, RBM ADL Self (Mohammed and Tashev, 2017) Body-worn CNN ADL, gesture Self (Morales and Roggen, 2016) Body-worn CNN ADL, smart home D01, D02 (Murad and Pyun, 2017) Body-worn RNN ADL, smart home D01, D02, D05, D14 (Ordóñez and Roggen, 2016) Body-worn CNN, RNN ADL, gesture, posture, factory D01, D02 (Panwar et al, 2017) Body-worn CNN ADL Self (Plötz et al, 2011) Body-worn, object RBM ADL, food preparation, factory D01, D02, D08, D14…”

Human Activity Recognition Based on Motion Sensor Using U-Net

“…The first one is video-based HAR, using video cameras to monitor the activity of the human body. Another is sensor-based HAR, which is based on time series data collected by sensors such as mobile phone built-in accelerometers [2][3][4] , wrist-worn accelerometers [5][6][7] , waist-mounted accelerometers [8][9] , gyroscopes and magnetometers [10] . Due to the wide use of portable and wearable sensors with low cost, low power consumption, high capacity and miniaturization, HAR based on sensor data has become a research hotspot.…”

“…Human activity recognition for time series is a complex process, which usually involves the following steps. First, preprocess the time series data such as smoothing, normalization [6] , and separating gravity component [18] from acceleration data. Then segment the…”

“…The convolution neural network is often used to analyze simple and complex activities from the time series data. The workload of feature engineering is greatly reduced by automatically learning and extracting features [6,32] .…”

Margin-Based Deep Learning Networks for Human Activity Recognition

“…Compared with traditional methods, deep learning networks can automatically extract high-dimensional features from raw sensor inputs. Although recent studies have achieved good classification performance [6][7][8], current HAR systems are still far from ideal. The first issue which we deal with in this work is that some human activities have intra-class diversity and inter-class similarity.…”