Wearable sensors have transformed how we monitor and classify human activities, offering real-time data collection for applications in health, sports, and human-computer interaction. This study provides an extensive review of motion data processing and classification techniques, focusing on methods like feature extraction and machine learning models. Wearable devices, such as smartwatches and fitness trackers, capture diverse data types, from accelerometer readings to heart rate variations, enabling precise activity recognition.
The study explores traditional feature extraction methods and advanced deep learning techniques, highlighting how they contribute to better performance in recognizing different activities. Deep learning models, especially convolutional neural networks (CNNs), have proven particularly effective in capturing complex motion patterns. However, challenges like data complexity, high-dimensional inputs, and privacy concerns remain.
Looking forward, the integration of multi-modal data and real-time processing capabilities presents promising directions for enhancing the accuracy and efficiency of motion classification. As wearable technology continues to evolve, it holds immense potential to revolutionize sectors like personalized healthcare and sports training, making everyday life smarter and more connected. This review aims to guide future research in maximizing the impact of wearable sensor data, making significant strides in the field of human activity recognition.
DOI: https://dx.doi.org/10.61927/igmin123
Full Text: https://www.igminresearch.com/articles/html/igmin123
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