Deep learning for human activity recognition: A resource efficient implementation on low-power devices

Ravì, Daniele; Wong, Charence; Lo, Benny; Yang, Guang‐Zhong

doi:10.1109/bsn.2016.7516235

Cited by 203 publications

(130 citation statements)

References 19 publications

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“…Ronao and Cho () have proposed multilayer CNN model with alternating convolutional and pooling layers and showed that their proposed model outperforms the state‐of‐the‐art accuracy for ADLs which were recorded by the authors from 30 different users. Ravi, Wong, Lo, and Yang () have used short‐term Fourier transform of the accelerometer data as an input to the proposed CNN network and have achieved accuracy close to the state‐of‐the‐art results. Bhattacharya and Lane () have designed and developed a restricted Boltzmann machine (RBM)‐based AR model for smart watches and have proved that the model does not have any hardware constraints.…”

Section: Deep Learning In Armentioning

confidence: 72%

Recent trends in machine learning for human activity recognition—A survey

Ramamurthy

Roy

2018

WIREs Data Min & Knowl

234

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There has been an upsurge recently in investigating machine learning techniques for activity recognition (AR) problems as they have been very effective in extracting and learning knowledge from the activity datasets. The technique ranges from heuristically derived hand-crafted feature-based traditional machine learning algorithms to the recently developed hierarchically self-evolving feature-based deep learning algorithms. AR continues to remain a challenging problem in uncontrolled smart environments despite the amount of work contributed by the researcher in this field. The complex, volatile, and chaotic nature of the activity data presents numerous challenges that influence the performance of the AR systems in the wild. In this article, we present a comprehensive overview of recent machine learning and data mining techniques generally employed for AR and the underpinning problems and challenges associated with the existing systems. We also articulate the recent advances and state-of-the-art techniques in this domain in an attempt to identify the possible directions for future AR research.

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Section: Deep Learning In Armentioning

confidence: 72%

Recent trends in machine learning for human activity recognition—A survey

Ramamurthy

Roy

2018

WIREs Data Min & Knowl

234

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“…In (Singh et al, 2017), pressure sensor data was transformed to the image via modality transformation. Other similar work include (Ravi et al, 2016;Li et al, 2016b). This model-driven approach can make use of the temporal correlation of sensor.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…• Public datasets: There are already many public HAR datasets that are adopted by most researchers (e.g. (Plötz et al, 2011;Ravi et al, 2016;Hammerla et al, 2016)). By summarizing existing literature, we present several widely used public datasets in Table 3.…”

Section: Benchmark Datasetsmentioning

confidence: 99%

Deep learning for sensor-based activity recognition: A survey

Wang

Chen

Hao

et al. 2019

Pattern Recognition Letters

1,547

927

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Research Highlights (Required)To create your highlights, please type the highlights against each \item command.It should be short collection of bullet points that convey the core findings of the article. It should include 3 to 5 bullet points (maximum 85 characters, including spaces, per bullet point.)• We survey deep learning based HAR in sensor modality, deep model, and application.• We comprehensively discuss the insights of deep learning models for HAR tasks.• We extensively investigate why deep learning can improve the performance of HAR.• We also summarize the public HAR datasets frequently used for research purpose.• We present some grand challenges and feasible solutions for deep learning based HAR. ABSTRACTSensor-based activity recognition seeks the profound high-level knowledge about human activities from multitudes of low-level sensor readings. Conventional pattern recognition approaches have made tremendous progress in the past years. However, those methods often heavily rely on heuristic hand-crafted feature extraction, which could hinder their generalization performance. Additionally, existing methods are undermined for unsupervised and incremental learning tasks. Recently, the recent advancement of deep learning makes it possible to perform automatic high-level feature extraction thus achieves promising performance in many areas. Since then, deep learning based methods have been widely adopted for the sensor-based activity recognition tasks. This paper surveys the recent advance of deep learning based sensor-based activity recognition. We summarize existing literature from three aspects: sensor modality, deep model, and application. We also present detailed insights on existing work and propose grand challenges for future research.

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“…In [16], the authors proposed a human activity recognition technique based on a deep learning model designed for low power devices. The recognition gives significant contextual data for well-being.…”

Section: Related Workmentioning

confidence: 99%

Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning

Koubâa

Ammar

Benjdira

et al. 2020

2020 6th Conference on Data Science and Machine Learning Applications (CDMA)

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In the Muslim community, the prayer (i.e. Salat) is the second pillar of Islam, and it is the most essential and fundamental worshiping activity that believers have to perform five times a day. From a gestures' perspective, there are predefined human postures that must be performed in a precise manner. However, for several people, these postures are not correctly performed, due to being new to Salat or even having learned prayers in an incorrect manner. Furthermore, the time spent in each posture has to be balanced. To address these issues, we propose to develop an artificial intelligence assistive framework that guides worshippers to evaluate the correctness of the postures of their prayers. This paper represents the first step to achieve this objective and addresses the problem of the recognition of the basic gestures of Islamic prayer using Convolutional Neural Networks (CNN). The contribution of this paper lies in building a dataset for the basic Salat positions, and train a YOLOv3 neural network for the recognition of the gestures. Experimental results demonstrate that the mean average precision attains 85% for a training dataset of 764 images of the different postures. To the best of our knowledge, this is the first work that addresses human activity recognition of Salat using deep learning.

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Deep learning for human activity recognition: A resource efficient implementation on low-power devices

Cited by 203 publications

References 19 publications

Recent trends in machine learning for human activity recognition—A survey

Recent trends in machine learning for human activity recognition—A survey

Deep learning for sensor-based activity recognition: A survey

Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning

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