Recent Progress in Sensing and Computing Techniques for Human Activity Recognition and Motion Analysis

Meng, Zhaozong; Zhou, Mingxing; Guo, Changxin; Fan, Qirui; Zhang, Hao; Gao, Nan; Zhang, Zonghua

doi:10.3390/electronics9091357

Cited by 39 publications

(18 citation statements)

References 101 publications

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“…From the literature analysis, it is evident that reported results are mostly simulated, whereas other works explore only partially the analysis of people's movements [40]. Several issues are still open:…”

Section: Open Challengesmentioning

confidence: 99%

Video Based Mobility Monitoring of Elderly People Using Deep Learning Models

et al. 2023

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In recent years, the number of older people living alone has increased rapidly. Innovative vision systems to remotely assess people's mobility can help healthy, active, and happy aging. In the related literature, the mobility assessment of older people is not yet widespread in clinical practice. In addition, the poor availability of data typically forces the analyses to binary classification, e.g. normal/anomalous behavior, instead of processing exhaustive medical protocols. In this paper, real videos of elderly people performing three mobility tests of a clinical protocol are automatically categorized, emulating the complex evaluation process of expert physiotherapists. Videos acquired using low-cost cameras are initially processed to obtain skeletal information. A proper data augmentation technique is then used to enlarge the dataset variability. Thus, significant features are extracted to generate a set of inputs in the form of time series. Four deep neural network architectures with feedback connections, even aided by a preliminary convolutional layer, are proposed to label the input features in discrete classes or to estimate a continuous mobility score as the result of a regression task. The best results are achieved by the proposed Conv-BiLSTM classifier, which achieves the best accuracy, ranging between 88.12% and 90%. Further comparisons with shallow learning classifiers still prove the superiority of the deep Conv-BiLSTM classifier in assessing people's mobility, since deep networks can evaluate the quality of test executions.

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Section: Open Challengesmentioning

confidence: 99%

Video Based Mobility Monitoring of Elderly People Using Deep Learning Models

et al. 2023

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“…There are different methods for building a model to do this. There are three types of classification methods: the threshold-based method, machine learning techniques and Deep Learning (DL) methods [ 23 ]. The threshold-based method is typically used to detect postures, movements and simple gestures, while machine learning techniques are data analysis methods whose model building is automated.…”

Section: Overview Of Related Workmentioning

confidence: 99%

Daily Human Activity Recognition Using Non-Intrusive Sensors

Ramos

Domingo

Zalama

et al. 2021

Sensors

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In recent years, Artificial Intelligence Technologies (AIT) have been developed to improve the quality of life of the elderly and their safety in the home. This work focuses on developing a system capable of recognising the most usual activities in the daily life of an elderly person in real-time to enable a specialist to monitor the habits of this person, such as taking medication or eating the correct meals of the day. To this end, a prediction model has been developed based on recurrent neural networks, specifically on bidirectional LSTM networks, to obtain in real-time the activity being carried out by the individuals in their homes, based on the information provided by a set of different sensors installed at each person’s home. The prediction model developed in this paper provides a 95.42% accuracy rate, improving the results of similar models currently in use. In order to obtain a reliable model with a high accuracy rate, a series of processing and filtering processes have been carried out on the data, such as a method based on a sliding window or a stacking and re-ordering algorithm, that are subsequently used to train the neural network, obtained from the public database CASAS.

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“…Te activity data collection methods are mainly divided into two groups: video images [8] and wearable sensors [9]. Te former acquires a series of human motion images through cameras and extracts human motion feature information from these images.…”

Section: Introductionmentioning

confidence: 99%

“…However, it has some disadvantages and limitations to the aforementioned data collection methods [9]. Te method based on video images needs to be completed under laboratory conditions since a specifc background is required, and the price of cameras is usually expensive.…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition Based on a Modified Capsule Network

Zhu

Chen

Liu

et al. 2023

Mobile Information Systems

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Human activity recognition (HAR) has attracted considerable research attention in the past decade with the development of wearable sensor technology and deep learning algorithms. However, most of the existing HAR methods ignored the spatial relationship of features, which may lead to recognition errors. In this paper, a novel model based on a modified capsule network (MCN) is proposed to accurately recognize various human activities. This novel model is composed of a convolution block and a capsule block, which can achieve end-to-end intelligent recognition. In the meantime, the spatial information among features is preserved through a dynamic routing process. To validate the effectiveness of the model, a human activity dataset is constructed by placing an inertial measurement unit (IMU) on the calf of the volunteers to collect their activity data in daily life, including walking, jogging, upstairs, downstairs, up-ramps, and down-ramps. The recognition accuracy of this novel approach can reach 96.08%, which performs better than the convolutional neural network (CNN) with an accuracy of 91.62%. In addition, it is evaluated on two public datasets named WISDM and UCI-HAR, and the accuracies achieve 98.21% and 95.28%, respectively, which presents higher accuracy than the reported results obtained from benchmark algorithms like CNN. The experimental results show that the proposed model has better activity detection capability and achieves outstanding performance for HAR.

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Recent Progress in Sensing and Computing Techniques for Human Activity Recognition and Motion Analysis

Cited by 39 publications

References 101 publications

Video Based Mobility Monitoring of Elderly People Using Deep Learning Models

Video Based Mobility Monitoring of Elderly People Using Deep Learning Models

Daily Human Activity Recognition Using Non-Intrusive Sensors

Human Activity Recognition Based on a Modified Capsule Network

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