Deep recurrent neural network for mobile human activity recognition with high throughput

Abstract

In this paper, we propose a method of human activity recognition with high throughput from raw accelerometer data applying a deep recurrent neural network (DRNN), and investigate various architectures and its combination to find the best parameter values. The "high throughput" refers to short time at a time of recognition. We investigated various parameters and architectures of the DRNN by using the training dataset of 432 trials with 6 activity classes from 7 people. The maximum recognition rate was 95.42% and 83.43% against the test data of 108 segmented trials each of which has single activity class and 18 multiple sequential trials, respectively. Here, the maximum recognition rates by traditional methods were 71.65% and 54.97% for each. In addition, the efficiency of the found parameters was evaluated by using additional dataset. Further, as for throughput of the recognition per unit time, the constructed DRNN was requiring only 1.347 [ms], while the best traditional method required 11.031 [ms] which includes 11.027 [ms] for feature calculation. These advantages are caused by the compact and small architecture of the constructed real time oriented DRNN.

Deep learning for sensor-based activity recognition: A survey

“…Few work used RNN for the HAR tasks (Hammerla et al, 2016;Inoue et al, 2016;Edel and Köppe, 2016;Guan and Ploetz, 2017), where the learning speed and resource consumption are the main concerns for HAR. (Inoue et al, 2016) investigated several model parameters first and then proposed a relatively good model which can perform HAR with high throughput. (Edel and Köppe, 2016) proposed a binarized-BLSTM-RNN model, in which the weight parameters, input, and output of all hidden layers are all binary values.…”

“…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 Action Recognition Using Deep Learning Methods on Limited Sensory Data

“…There are plenty studies that take advantage of interpreting capability of sequential data for activity recognition by using wearables [38] [39]. When implementing LSTM models, most commonly used architecture is described in [9].…”

Few-shot learning-based human activity recognition

“…Second, DL models can be reused for similar tasks, which makes HAR model construction more efficient. Different DL models such as deep neural networks [26,27], convolutional neural networks [10,28], autoencoders [11,29], restricted Boltzmann machines [12,30], and recurrent neural networks [31,32] have been applied in HAR. We refer readers to [8] for more details on DL-based HAR.…”