Highly-accurate binary tiny neural network for low-power human activity recognition

Vita, Antonio De; Pau, Danilo; Benedetto, Luigi Di; Rubino, Alfredo; Pétrot, Frédéric; Licciardo, Gian Domenico

doi:10.1016/j.micpro.2021.104371

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…7. The authors in [1,5,8,12,13,15,17,22,24,33,47] have applied their proposed model on the WISDM, PAMAP2, and KU-HAR datasets, respectively. Moreover, we have also used the same experimental datasets but in the context of different activity types.…”

Section: Experimental Results Of the Deep-har Modelmentioning

confidence: 99%

“…The best recognition performance was obtained by the Att-based Residual Network with an accuracy of 98.85% and followed by UDR-RC with 97.50% accuracy. The authors in [5,12,15,17,24] have used the one-shot learning methods, DELAPAR, Att-based Residual Network, ResNet+HC, and float CNN on PAMAP2 experimental dataset. In [15], the DELAPAR achieved the highest accuracy rate of 96.62%, followed by the Att-based Residual Network [12] with an accuracy rate of 96.62%.…”

Section: Experimental Results Of the Deep-har Modelmentioning

confidence: 99%

“…These models achieved the accuracy rate of 97.50%, 94.20%, 98.85%, 95.78%, and 94.60%, respectively. For the PAMAP2 dataset, the researchers have recently used the One-shot learning methods [24], Deep Learning Architecture for Physical Activity Recognition (DELAPAR) [15], Att-based Residual Network [12], Residual Network and Heterogeneous CNN (ResNet+ HC) [17], and float CNN [5] with an accuracy rate of 84.41%, 96.62%, 93.16%, 92.97%, and 85.23%, respectively. The KU-HAR dataset was used by the RF classifier [33] and the transformer model [8], with accuracy rates of 90.00% and 99.20%, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In Eqs. ( 4), (5), and (6), the split datasets X dn , Y dn , and Z dn consists of (t) number of observations, where n = dataset number.…”

Section: Problem Statementmentioning

confidence: 99%

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Deep-HAR: an ensemble deep learning model for recognizing the simple, complex, and heterogeneous human activities

Kumar

Suresh

2023

Multimed Tools Appl

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The recognition of human activities has become a dominant emerging research problem and widely covered application areas in surveillance, wellness management, healthcare, and many more. In real life, the activity recognition is a challenging issue because human beings are often performing the activities not only simple but also complex and heterogeneous in nature. Most of the existing approaches are addressing the problem of recognizing only simple straightforward activities (e.g. walking, running, standing, sitting, etc.). Recognizing the complex and heterogeneous human activities are a challenging research problem whereas only a limited number of existing works are addressing this issue. In this paper, we proposed a novel Deep-HAR model by ensembling the Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for recognizing the simple, complex, and heterogeneous type activities. Here, the CNNs are used for extracting the features whereas RNNs are used for finding the useful patterns in time-series sequential data. The activities recognition performance of the proposed model was evaluated using three different publicly available datasets, namely WISDM, PAMAP2, and KU-HAR. Through extensive experiments, we have demonstrated that the proposed model performs well in recognizing all types of activities and has achieved an accuracy of 99.98%, 99.64%, and 99.98% for simple, complex, and heterogeneous activities respectively.

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Section: Experimental Results Of the Deep-har Modelmentioning

confidence: 99%

Section: Experimental Results Of the Deep-har Modelmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…In Eqs. ( 4), (5), and (6), the split datasets X dn , Y dn , and Z dn consists of (t) number of observations, where n = dataset number.…”

Section: Problem Statementmentioning

confidence: 99%

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Deep-HAR: an ensemble deep learning model for recognizing the simple, complex, and heterogeneous human activities

Kumar

Suresh

2023

Multimed Tools Appl

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“…For example, the model has the characteristics of complex structure, time-consuming training, and the HAR optimization without considering sample augmentation. The HAR based on hardware design and sensor data stream analysis can well solve the problems of device deployment, low-power consumption, and computational resource constraints [14]- [16], but it is usually not widely applicable. Human activities present the characteristics of diversity, complexity, and real-time.…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition Based on Multichannel Convolutional Neural Network With Data Augmentation

2022

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In view of the excellent portability and privacy protection of wearable sensor devices, human activity recognition (HAR) of wearable devices has increased applications in human-computer interaction, health care, etc. Therefore, it is necessary to recognize various human activities accurately and efficiently. In this paper, we propose a multi-channel convolutional neural network with data augmentation for HAR, denoted AMC-CNN. First, the sliding windows in time series are used to construct the feature window, and then the feature window is augmented by data transformation and data addition. Through the horizontal connection of the augmented feature windows, the reconstructed feature samples are obtained. Second, a relatively lightweight multi-channel convolutional neural network is designed. In order to mine deep correlations between sensor data more efficiently, we use reasonable convolution kernel settings to compute multi-channel convolutions and extract parallel multi-scale features, and then train the model to recognize human activities. Finally, experiments are carried out on WISDM and MHEALTH datasets to verify the effectiveness of the data augmentation. Moreover, the other three models are adopted for comparative experiments. The evaluation indexes show that the method proposed in this paper has a better recognition effect, which verifies that the AMC-CNN model is effective in both single-sensor and multi-sensor HAR.INDEX TERMS Multi-channel convolutional neural network, data augmentation, human activity recognition, wearable devices.

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A 0.8 mW TinyML-Based PDM-to-PCM Conversion for In-Sensor KWS Applications

Vitolo

Liguori

Benedetto

et al. 2023

Lecture Notes in Electrical Engineering

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Highly-accurate binary tiny neural network for low-power human activity recognition

Cited by 4 publications

References 19 publications

Deep-HAR: an ensemble deep learning model for recognizing the simple, complex, and heterogeneous human activities

Deep-HAR: an ensemble deep learning model for recognizing the simple, complex, and heterogeneous human activities

Human Activity Recognition Based on Multichannel Convolutional Neural Network With Data Augmentation

A 0.8 mW TinyML-Based PDM-to-PCM Conversion for In-Sensor KWS Applications

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