Human Activity and Gesture Recognition: A Review

Saini, Rashmi; Maan, Vinod

doi:10.1109/iconc345789.2020.9117535

Cited by 13 publications

(4 citation statements)

References 21 publications

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“…Furthermore, they can longitudinally track rural elderly behavior patterns, identify potential health issues, and offer data support for personalized medical care, thereby enhancing the quality of life and care effectiveness for rural elderly. Applying these algorithms in rural elderly care not only improves the accuracy and timeliness of monitoring but also alleviates the burden on healthcare personnel, fostering a safer and more compassionate living environment (Saini et al, 2023).…”

Section: Human Activity Recognitionmentioning

confidence: 99%

Home Activity Recognition for Rural Elderly Based on Deep Learning and Smartphone Sensors

Zhang,

Tong,

Lin

2024

Journal of Organizational and End User Computing

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With the exacerbation of the rural aging population trend, home-based health monitoring for the rural elderly has become a societal focal point, demanding an effective technological means to elevate the level of rural elderly health management. However, traditional algorithms for monitoring rural elderly behavior face myriad challenges, such as effectively capturing temporal and spatial features. Consequently, addressing the need to enhance the accuracy and robustness of rural elderly behavior recognition has become an urgent problem to solve. This study responds to this challenge by comprehensively employing deep learning and temporal modeling techniques, designing, and validating a short-term and long-term dual-layer home-based health monitoring system for the rural elderly.In the short-term layer, the model utilizes smartphones to collect health information from the rural elderly in various ways and performs real-time anomaly behavior detection.

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Section: Human Activity Recognitionmentioning

confidence: 99%

Home Activity Recognition for Rural Elderly Based on Deep Learning and Smartphone Sensors

Zhang,

Tong,

Lin

2024

Journal of Organizational and End User Computing

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“…Much of the work in this field has focused exclusively on activity transition recognition for a whole range of applications. [12] Some activity transition recognition techniques leverage the concept of a "smart home," using networks of sensors placed strategically around the user's home to detect changes in their current activity of daily living. [13] Often this is done by tracking the user's location, as they move from bedroom to kitchen to living room -however, this gives only a high-level classification of the activity transition without observing specific movements.…”

Section: B Recognizing the Activity Transitionmentioning

confidence: 99%

A Systematic Literature Review of Intent Sensing for Control of Medical Devices

Russell

Bergmann

2022

IEEE Trans. Med. Robot. Bionics

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The usefulness of medical devices, which require user input, is often limited by the control schemes that operate them. The recognition of user intent could enable far more intuitive control schemes that respond automatically to what the user wants the device to do. This paper provides a definition for intent, and then aims to systematically review current methods for sensing intent. It compares the accuracy of different methods and discusses how they might be combined. A systematic literature search was performed using IEEE Xplore, PubMed and Web of Science databases. 2311 papers were considered, reduced to 155 after review. All selected papers were assessed for quality using a checklist. The results identified and compared 15 sensing modalities used for intent sensing in a range of situations and applications that broadly fell into 12 distinct categories, with highly varying levels of accuracy. Several papers reached accuracy levels that could be suitable for everyday clinical application, but most work done on intent sensing to date has focused on activity transition classification, with fewer papers addressing task goal interference or predicting future actions. Further work can focus on the implementation of these kind of methods into a combined, context-aware intent-sensing control system.

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“…In particular, activity recognition serves as the basis for the development of potential applications in the sectors of health and wellness as well as sports. HAR (Human Activity Recognition), which stands for Human Activity Recognition, has a wide variety of current uses thanks to the positive impact it has on people's health (4) . The complexity of human presentation adds another layer of difficulty to the study of human behavior through activity.…”

Section: Introductionmentioning

confidence: 99%

Hyper-Parameters Activation on Machine Learning Algorithms to Improve the Recognition of Human Activities with IoT Sensor Dataset

Kumar,

Rajesh

2023

IJST

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Objectives: To enhance the accuracy of the Human Activity Recognition (HAR) in the computer vision. The study aims to explore the practical applications of HAR in various fields, including gaming, rehabilitation, health monitoring, human-robot interaction, sports, video surveillance, and robotics. The specific objective is to develop a system that can accurately recognize and categorize the human actions thereby enabling the design and implementation of effective human-computer interaction systems. Methods: In this research, a HAR module is developed to extract relevant features such as Accelerometer, Gyroscope and Magnetometer features from signals obtained from IoT wearable sensors. 7352 samples are considered. Machine learning models such as K-Nearest Neighbor Algorithm (KNN), Random Forest Algorithm (RF), Naive Bayes Algorithm (NB), Decision Tree Algorithm (DT), Multi-Layer Perceptron (MLP) Algorithm and Proposed Algorithm (MLP classifier with adam optimizer and relu activation) are employed to automatically recognize and classify activities performed by individuals based on the raw data. The models are trained and evaluated using statistical parameters, including accuracy score, precision, recall, and f1-score. A comparison is made between existing approaches such as KNN, RF, NB, DT and MLP Algorithms and the proposed model to assess its performance. Findings: The study reveals that the proposed model MLP classifier with adam optimizer and relu activation is effective in recognizing and categorizing the human activities with 95% of accuracy. The statistical evaluation metrices demonstrates the reliability and robustness of the proposed model with 96% of precision. Novelty: This research contributes to the field of Human Activity Recognition by presenting a novel approach that combines the IoT wearable sensors, feature extraction, and machine learning models to accurately recognize and classify human actions. The proposed model outperforms the existing methods in terms of accuracy. The study highlights the importance of the proposed algorithm and its potential for improving the human computer interaction systems.

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Human Activity and Gesture Recognition: A Review

Cited by 13 publications

References 21 publications

Home Activity Recognition for Rural Elderly Based on Deep Learning and Smartphone Sensors

Home Activity Recognition for Rural Elderly Based on Deep Learning and Smartphone Sensors

A Systematic Literature Review of Intent Sensing for Control of Medical Devices

Hyper-Parameters Activation on Machine Learning Algorithms to Improve the Recognition of Human Activities with IoT Sensor Dataset

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