Towards EMG control interface for smart garments

Benatti, Simone; Farella, Elisabetta; Benini, Luca

doi:10.1145/2641248.2641352

Cited by 20 publications

(14 citation statements)

References 22 publications

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“…In the context of bio-signal analysis, DWT has been used in state-of-the-art research works to extract features from physiological data since they provide a good trade-off between performance and signal-to-noise ratio [14]. We use DWT with three different window lengths: 2 s, 4 s, or 8 s. Our multicore platform (Sec.…”

Section: A Pre-processing and Feature Extractionmentioning

confidence: 99%

Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices

Ingolfsson

Cossettini

Wang

et al. 2021

2021 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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We present the implementation of seizure detection algorithms based on a minimal number of EEG channels on a parallel ultra-low-power embedded platform. The analyses are based on the CHB-MIT dataset, and include explorations of different classification approaches (Support Vector Machines, Random Forest, Extra Trees, AdaBoost) and different pre/postprocessing techniques to maximize sensitivity while guaranteeing no false alarms. We analyze global and subject-specific approaches, considering all 23-electrodes or only 4 temporal channels. For 8 s window size and subject-specific approach, we report zero false positives and 100% sensitivity. These algorithms are parallelized and optimized for a parallel ultra-low power (PULP) platform, enabling 300h of continuous monitoring on a 300 mAh battery, in a wearable form factor and power budget. These results pave the way for the implementation of affordable, wearable, long-term epilepsy monitoring solutions with low falsepositive rates and high sensitivity, meeting both patient and caregiver requirements.

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Section: A Pre-processing and Feature Extractionmentioning

confidence: 99%

Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices

Ingolfsson

Cossettini

Wang

et al. 2021

2021 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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“…DWT is a signal decomposition technique for timefrequency analyses [26] that has been reported to work well for seizure detection [21]. DWT offers a good trade-off between signal-to-noise ratio and performance [27], and it allows to obtain both frequency and temporal features of a signal by applying a cascade series Mother Wavelets filters.…”

Section: B Pre-processing and Feature Extractionmentioning

confidence: 99%

Energy-Efficient Tree-Based EEG Artifact Detection

Ingolfsson¹,

Cossettini²,

Benatti³

et al. 2022

Preprint

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In the context of epilepsy monitoring, EEG artifacts are often mistaken for seizures due to their morphological similarity in both amplitude and frequency, making seizure detection systems susceptible to higher false alarm rates. In this work we present the implementation of an artifact detection algorithm based on a minimal number of EEG channels on a parallel ultra-low-power (PULP) embedded platform. The analyses are based on the TUH EEG Artifact Corpus dataset and focus on the temporal electrodes. First, we extract optimal feature models in the frequency domain using an automated machine learning framework, achieving a 93.95% accuracy, with a 0.838 F1 score for a 4 temporal EEG channel setup. The achieved accuracy levels surpass state-of-the-art by nearly 20%. Then, these algorithms are parallelized and optimized for a PULP platform, achieving a 5.21× improvement of energy-efficient compared to state-of-theart low-power implementations of artifact detection frameworks. Combining this model with a low-power seizure detection algorithm would allow for 300h of continuous monitoring on a 300 mAh battery in a wearable form factor and power budget. These results pave the way for implementing affordable, wearable, long-term epilepsy monitoring solutions with low false-positive rates and high sensitivity, meeting both patients' and caregivers' requirements.Clinical relevance-The proposed EEG artifact detection framework can be employed on wearable EEG recording devices, in combination with EEG-based epilepsy detection algorithms, for improved robustness in epileptic seizure detection scenarios.

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“…The MYO gesture recognition armband was developed by Canada's Thalmic Labs in 2013. It uses a myoelectric sensor and nine-axis detector to detect more than 20 hand movements for controlling computer games, robots, a mouse, and more (Benatti et al 2014). In addition, the smart fitness clothing developed by Athos has a total of 26 sEMG and heart rate sensors to monitor muscle fatigue as a judgment for sports injury prevention.…”

Section: Semg Signal Applicationmentioning

confidence: 99%

Designing and Evaluating a Wearable Semg Device for the Elderly

2021

Proceedings 14th International Conference on ICT, Society and Human Beings (ICT 2021), the 18th International Conference Web Ba

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As muscle and strength decline with age, the risks of falling down and becoming disabled increase for the elderly. However, rehabilitation for patients through long-term treatments and assessments in medical institutions take a long time. It reduces the elderly people's quality of life and causes a burden on their living and financial on their family. Therefore, the goal of this study is to design the wearable sEMG device based on wireless sensing technology for muscle rehabilitation. We aim to help the elderly train their lower limbs at home and simultaneously collect physiological signals though training for remote medical diagnosis. In this study, the design direction is established through focus group interviews. The rehabilitation actions were observed, and the position of muscle movement was measured to establish design parameters and procedures. The designed wearable device was evaluated through a pilot study and semi-structured interview to understand its usability and feasibility. Finally, experts evaluated the usability of the device. From the results, it was found that rehabilitation at home using the designed wearable sEMG device was accepted by most of the elderly respondents. They think that it is very convenient to receive medical feedback at home. Furthermore, medical staff can also use the physiological data to understand the patient's condition and provide diagnosis and treatment. We hope that the product can be widely used to receive signals of the rehabilitation of elderly's lower limb muscles at home in the future. Further discussions on the design implications of the development process can be a reference for future related product development.

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Towards EMG control interface for smart garments

Cited by 20 publications

References 22 publications

Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices

Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices

Energy-Efficient Tree-Based EEG Artifact Detection

Designing and Evaluating a Wearable Semg Device for the Elderly

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