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Wearable online freezing of gait detection and cueing system
Jan Slemenšek, Jelka Geršak, Božidar Bratina, Vesna M. Van Midden, Zvezdan Pirtošek, Riko Šafarič, 2024, original scientific article

Abstract: This paper presents a real-time wearable system designed to assist Parkinson’s disease patients experiencing freezing of gait episodes. The system utilizes advanced machine learning models, including convolutional and recurrent neural networks, enhanced with past sample data preprocessing to achieve high accuracy, efficiency, and robustness. By continuously monitoring gait patterns, the system provides timely interventions, improving mobility and reducing the impact of freezing episodes. This paper explores the implementation of a CNN+RNN+PS machine learning model on a microcontroller-based device. The device operates at a real-time processing rate of 40 Hz and is deployed in practical settings to provide ‘on demand’ vibratory stimulation to patients. This paper examines the system’s ability to operate with minimal latency, achieving an average detection delay of just 261 milliseconds and a freezing of gait detection accuracy of 95.1%. While patients received on-demand stimulation, the system’s effectiveness was assessed by decreasing the average duration of freezing of gait episodes by 45%. These preliminarily results underscore the potential of personalized, real-time feedback systems in enhancing the quality of life and rehabilitation outcomes for patients with movement disorders.
Keywords: Parkinson’s disease, freezing of gait, machine learning, real-time systems, wearable devices, on-demand stimulation
Published in DKUM: 31.01.2025; Views: 0; Downloads: 7
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Exercise repetition detection for resistance training based on smartphones
Igor Pernek, Karin Anna Hummel, Peter Kokol, 2012, original scientific article

Abstract: Regular exercise is one of the most important factors in maintaining a good state of health. In the past, different systems have been proposed to assist people when exercising. While most of those systems focus only on cardio exercises such as running and cycling, we exploit smartphones to support leisure activities with a focus on resistance training. We describe how off-the-shelf smartphones without additional external sensors can be leveragedto capture resistance training data and to give reliable training feedback. We introduce a dynamic time warping-based algorithm to detect individual resistance training repetitions from the smartphoneʼs acceleration stream. We evaluate the algorithm in terms of the number of correctly recognized repetitions. Additionally, for providing feedback about the qualityof repetitions, we use the duration of an individual repetition and analyze how accurately start and end times of repetitions can be detected by our algorithm. Our evaluations are based on 3,598 repetitions performed by tenvolunteers exercising in two distinct scenarios, a gym and a natural environment. The results show an overall repetition miscount rate of about 1 %and overall temporal detection error of about 11 % of individual repetition duration.
Keywords: wearable systems, resistance training, smartphone, accelerometer
Published in DKUM: 10.07.2015; Views: 1765; Downloads: 103
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