Approaches and algorithms for activity recognition have recently made substantial progress due to advancements in pervasive and mobile computing, smart environments and ambient assisted living. Nevertheless, it is sti...
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Approaches and algorithms for activity recognition have recently made substantial progress due to advancements in pervasive and mobile computing, smart environments and ambient assisted living. Nevertheless, it is still difficult to achieve real-time continuous activity recognition as sensor data segmentation remains a challenge. This paper presents a novel approach to real-time sensor data segmentation for continuous activity recognition. Central to the approach is a dynamic segmentation model, based on the notion of varied time windows, which can shrink and expand the segmentation window size by using temporal information of sensordata and activities as well as the state of activity recognition. The paper first analyzes the characteristics of activities of daily living from which the segmentation model that is applicable to a wide range of activity recognition scenarios is motivated and developed. It then describes the working mechanism and relevant algorithms of the model in the context of knowledge-driven activity recognition based on ontologies. The presented approach has been implemented in a prototype system and evaluated in a number of experiments. Results have shown average recognition accuracy above 83% in all experiments for real time activity recognition, which proves the approach and the underlying model. (C) 2012 Elsevier B.V. All rights reserved.
Smartwatch sensors for human activity recognition (HAR) have gained significant attention due to their applications in healthcare and fitness monitoring. The effectiveness of HAR systems largely depends on the choice ...
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ISBN:
(纸本)9798350391749
Smartwatch sensors for human activity recognition (HAR) have gained significant attention due to their applications in healthcare and fitness monitoring. The effectiveness of HAR systems largely depends on the choice of sliding window widths for sensor data segmentation. This study investigates the impact of varying sliding window widths on the accuracy of HAR using wristwatch sensors and deep learning techniques. We conducted experiments using the daily human activity (DHA) dataset, comprising sensordata from 11 distinct activities. data was preprocessed and segmented using window sizes ranging from 5 to 40 seconds. Four deep learning models (CNN, LSTM, BiLSTM, and CNN-LSTM) were employed and evaluated using accuracy, precision, recall, and F1-score. Window size significantly affected HAR performance. Smaller windows improved short-duration activity recognition but increased computational complexity, while larger windows reduced computational load but decreased accuracy for rapid activity changes. The CNN-LSTM hybrid model consistently outperformed other models, achieving 92.11% accuracy with a 20-second window and overlapping segmentation. This research provides valuable insights into balancing recognition accuracy and computational resources in smartwatch sensor-based HAR, contributing to the development of efficient and accurate systems for real-world applications.
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