Daily Activities Classification on Human Motion Primitives Detection Dataset

Chin, Zi Hau; Ng, Hu; Yap, Timothy Tzen Vun; Tong, Hau-Lee; Ho, Chiung Ching; Goh, Vik Tor

doi:10.1007/978-981-13-2622-6_12

Cited by 10 publications

(3 citation statements)

References 11 publications

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“…This section looks at the recent literature in various forms of human motion detection and where machine learning has been applied. The articles in [28][29][30] collected a range of human activities where the test subjects were using wearable accelerometer on their wrists. The dataset collected by these activities were then run through the machine learning algorithms Random Forest, K Nearest Neighbours (KNN) and Support Vector Machine.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare

Taylor

Shah

Dashtipour

et al. 2020

Sensors

132

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Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real-time monitoring by deploying equipment on a person’s body. However, putting devices on a person’s body all the time makes it uncomfortable and the elderly tend to forget to wear them, in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in a quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals present particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software-defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine-learning model was able to achieve 96.70% accuracy using the Random Forest algorithm using 10 fold cross-validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90%. The machine-learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities.

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Section: Related Workmentioning

confidence: 99%

An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare

Taylor

Shah

Dashtipour

et al. 2020

Sensors

132

View full text Add to dashboard Cite

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“…This section examines state-of-the-art research in different types of human activity recognition as well as applications of Machine Learning (ML). According to the articles [34,35], a variety of human activities were observed and recorded while the participants wore wearable sensors such as an accelerometer. Once the data were gathered via distinct activities, it was treated with cutting-edge ML algorithms, for instance, Support Vector Machine (SVM), k-Nearest Neighbors (K-NN), and an ensemble learning approach called Random Forest (RF).…”

Section: Literature Reviewmentioning

confidence: 99%

Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments for Independent Assistive Living

Saeed

Shah

et al. 2021

Electronics

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Human activity monitoring is essential for a variety of applications in many fields, particularly healthcare. The goal of this research work is to develop a system that can effectively detect fall/collapse and classify other discrete daily living activities such as sitting, standing, walking, drinking, and bending. For this paper, a publicly accessible dataset is employed, which is captured at various geographical locations using a 5.8 GHz Frequency-Modulated Continuous-Wave (FMCW) RADAR. A total of ninety-nine participants, including young and elderly individuals, took part in the experimental campaign. During data acquisition, each aforementioned activity was recorded for 5–10 s. Through the obtained data, we generated the micro-doppler signatures using short-time Fourier transform by exploiting MATLAB tools. Subsequently, the micro-doppler signatures are validated, trained, and tested using a state-of-the-art deep learning algorithm called Residual Neural Network or ResNet. The ResNet classifier is developed in Python, which is utilised to classify six distinct human activities in this study. Furthermore, the metrics used to analyse the trained model’s performance are precision, recall, F1-score, classification accuracy, and confusion matrix. To test the resilience of the proposed method, two separate experiments are carried out. The trained ResNet models are put to the test by subject-independent scenarios and unseen data of the above-mentioned human activities at diverse geographical spaces. The experimental results showed that ResNet detected the falling and rest of the daily living human activities with decent accuracy.

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“…Each HAR stage has been analyzed in order to determine its impact on energy consumption as follows:Data collection and filtering stage: Firstly, in the data collection and filtering stage the set of used sensors affects energy consumption [62,110]. The reduction in the number of sensors can help improve the energy efficiency of the sensor device [61], whilst adding new sensor-type events can improve accuracy [54].…”

Section: Impact Of Human Activity Recognition (Har) Stages On Enermentioning

confidence: 99%

Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

Dinarevic

Husić

Baraković

2019

Sensors

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Human activity recognition (HAR) is a classification process that is used for recognizing human motions. A comprehensive review of currently considered approaches in each stage of HAR, as well as the influence of each HAR stage on energy consumption and latency is presented in this paper. It highlights various methods for the optimization of energy consumption and latency in each stage of HAR that has been used in literature and was analyzed in order to provide direction for the implementation of HAR in health and wellbeing applications. This paper analyses if and how each stage of the HAR process affects energy consumption and latency. It shows that data collection and filtering and data segmentation and classification stand out as key stages in achieving a balance between energy consumption and latency. Since latency is only critical for real-time HAR applications, the energy consumption of sensors and devices stands out as a key challenge for HAR implementation in health and wellbeing applications. Most of the approaches in overcoming challenges related to HAR implementation take place in the data collection, filtering and classification stages, while the data segmentation stage needs further exploration. Finally, this paper recommends a balance between energy consumption and latency for HAR in health and wellbeing applications, which takes into account the context and health of the target population.

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Daily Activities Classification on Human Motion Primitives Detection Dataset

Cited by 10 publications

References 11 publications

An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare

An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare

Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments for Independent Assistive Living

Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

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