HDL: Hierarchical Deep Learning Model based Human Activity Recognition using Smartphone Sensors

Su, Tongtong; Sun, Hongzan; Ma, Chunmei; Jiang, Lifen; Xu, Tongtong

doi:10.1109/ijcnn.2019.8851889

Cited by 19 publications

(9 citation statements)

References 25 publications

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“…The minimum precision of the laboratory experiment from the WISDM dataset was 0.928. Results [ 36 ] using deep bidirectional long term short term memory combined with CNN resulted in an accuracy of 0.980, which may not be feasible for battery powered field devices. Additionally, using a two channel CNN resulted in an accuracy of 0.953, with both channels using the UCI public dataset on human activity recognition [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environments

Russell

McDaid

Toscano

et al. 2021

Sensors

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Goal: To develop and validate a field-based data collection and assessment method for human activity recognition in the mountains with variations in terrain and fatigue using a single accelerometer and a deep learning model. Methods: The protocol generated an unsupervised labelled dataset of various long-term field-based activities including run, walk, stand, lay and obstacle climb. Activity was voluntary so transitions could not be determined a priori. Terrain variations included slope, crossing rivers, obstacles and surfaces including road, gravel, clay, mud, long grass and rough track. Fatigue levels were modulated between rested to physical exhaustion. The dataset was used to train a deep learning convolutional neural network (CNN) capable of being deployed on battery powered devices. The human activity recognition results were compared to a lab-based dataset with 1,098,204 samples and six features, uniform smooth surfaces, non-fatigued supervised participants and activity labelling defined by the protocol. Results: The trail run dataset had 3,829,759 samples with five features. The repetitive activities and single instance activities required hyper parameter tuning to reach an overall accuracy 0.978 with a minimum class precision for the one-off activity (climbing gate) of 0.802. Conclusion: The experimental results showed that the CNN deep learning model performed well with terrain and fatigue variations compared to the lab equivalents (accuracy 97.8% vs. 97.7% for trail vs. lab). Significance: To the authors knowledge this study demonstrated the first successful human activity recognition (HAR) in a mountain environment. A robust and repeatable protocol was developed to generate a validated trail running dataset when there were no observers present and activity types changed on a voluntary basis across variations in terrain surface and both cognitive and physical fatigue levels.

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Section: Discussionmentioning

confidence: 99%

Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environments

Russell

McDaid

Toscano

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…A multi-layer parallel LSTM network [30] 94.34 Convolutional neural network [31] 94.79 Convolutional neural network [16] 95.31 Fully convolutional network [32] 96.32 Genetic algorithm to optimize feature vector [33] 96.38 Bidirectional LSTM network [36] 92.67 CNN [37] 94.00 Hierarchical deep learning model [38] 97.95 Our method (LDA + SVM)…”

Section: Methodsmentioning

confidence: 99%

“…Dynamic time warping [23] 89.00 Handcrafted features + SVM [24] 89.00 Convolutional neural network [25] 90.89 Hidden Markov models [26] 91.76 PCA + SVM [27] 91.82 Stacked autoencoders + SVM [28] 92.16 Hierarchical continuous HMM [28] 93.18 Bidir-LSTM network [29] 93.79 A multi-layer parallel LSTM network [30] 94.34 Convolutional neural network [31] 94.79 Convolutional neural network [16] 95.31 Fully convolutional network [32] 96.32 Genetic algorithm to optimize feature vector [33] 96.38 Bidirectional LSTM network [34] 92.67 CNN [35] 94.00 Hierarchical deep learning model [36] 97.95 Our method (LDA + SVM)…”

Section: Recognition Accuracy (%)mentioning

confidence: 99%

An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition

Chen

Huang

2020

Sensors

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In recent years, sensors in smartphones have been widely used in applications, e.g., human activity recognition (HAR). However, the power of smartphone constrains the applications of HAR due to the computations. To combat it, energy efficiency should be considered in the applications of HAR with smartphones. In this paper, we improve energy efficiency for smartphones by adaptively controlling the sampling rate of the sensors during HAR. We collect the sensor samples, depending on the activity changing, based on the magnitude of acceleration. Besides that, we use linear discriminant analysis (LDA) to select the feature and machine learning methods for activity classification. Our method is verified on the UCI (University of California, Irvine) dataset; and it achieves an overall 56.39% of energy saving and the recognition accuracy of 99.58% during the HAR applications with smartphone.

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“…Other works have explored the effect of deepness on recognition, the authors in [12] proposed an HDL: Hierarchical Deep Learning Model capable of recognizing activities with an accuracy of 97.95 % on the UCI HAR dataset, their model is composed of several BLSTM layers, which are used to capture information from the original data, CNN layers came afterwards to learn features from the output of the last BLSTM layer, and classification is obtained in the end using a Softmax layer. Xu et al [13] have proposed InnoHAR, a network which, takes advantage of Inception-like modules to make feature extraction, combined with GRU for sequential temporal dependencies extraction, Gao et al [14] proposed a method called DanHAR designed for challenging scenarios where there are multi-modal sensors.…”

Section: Review Of Literature IImentioning

confidence: 99%

A Hybrid Deep Neural Network for Human Activity Recognition based on IoT Sensors

Benhaili¹,

Balouki²,

Moumoun³

2021

IJACSA

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Internet of things (IOT) sensors, has received a lot of interest in recent years due to the rise of application demands in domains like ubiquitous and context-aware computing, activity surveillance, ambient assistive living and more specifically in Human activity recognition. The recent development in deep learning allows to extract high-level features automatically, and eliminates the reliance on traditional machine learning techniques, which depended heavily on hand crafted features. In this paper, we introduce a network that can identify a variety of everyday human actions that can be carried out in a smart home environment, by using raw signals generated from Internet of Thing's motion sensors. We design our architecture basing on a combination of convolutional neural network (CNN) and Gated recurrent unit (GRU) layers. The CNN is first deployed to extract local and scale-invariance features, then the GRU layers are used to extract sequential temporal dependencies. We tested our model called (CNGRU) on three public datasets. It achieves an accuracy better or comparable to existing state of the art models.

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HDL: Hierarchical Deep Learning Model based Human Activity Recognition using Smartphone Sensors

Cited by 19 publications

References 25 publications

Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environments

Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environments

An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition

A Hybrid Deep Neural Network for Human Activity Recognition based on IoT Sensors

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