Long short-term memory (LSTM) recurrent neural network for muscle activity detection

Ghislieri, Marco; Cerone, Giacinto Luigi; Knaflitz, Marco; Agostini, Valentina

doi:10.1186/s12984-021-00945-w

Cited by 29 publications

(25 citation statements)

References 58 publications

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“…The advance in deep learning methods such as long short-term memory (LSTM) recurrent neural networks seems promising to detect temporal muscle activity (Ghislieri et al, 2021 ). Anyway, every approach algorithm will require extensive retraining, cross-validating, and the addition of social factors that may improve the model's accuracy and feasibility.…”

Section: Discussionmentioning

confidence: 99%

Classification of elderly pain severity from automated video clip facial action unit analysis: A study from a Thai data repository

Gomutbutra

Kittisares²,

Sanguansri³

et al. 2022

Front. Artif. Intell.

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Data from 255 Thais with chronic pain were collected at Chiang Mai Medical School Hospital. After the patients self-rated their level of pain, a smartphone camera was used to capture faces for 10 s at a one-meter distance. For those unable to self-rate, a video recording was taken immediately after the move that causes the pain. The trained assistant rated each video clip for the pain assessment in advanced dementia (PAINAD). The pain was classified into three levels: mild, moderate, and severe. OpenFace© was used to convert the video clips into 18 facial action units (FAUs). Five classification models were used, including logistic regression, multilayer perception, naïve Bayes, decision tree, k-nearest neighbors (KNN), and support vector machine (SVM). Out of the models that only used FAU described in the literature (FAU 4, 6, 7, 9, 10, 25, 26, 27, and 45), multilayer perception is the most accurate, at 50%. The SVM model using FAU 1, 2, 4, 7, 9, 10, 12, 20, 25, and 45, and gender had the best accuracy of 58% among the machine learning selection features. Our open-source experiment for automatically analyzing video clips for FAUs is not robust for classifying pain in the elderly. The consensus method to transform facial recognition algorithm values comparable to the human ratings, and international good practice for reciprocal sharing of data may improve the accuracy and feasibility of the machine learning's facial pain rater.

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Section: Discussionmentioning

confidence: 99%

Classification of elderly pain severity from automated video clip facial action unit analysis: A study from a Thai data repository

Gomutbutra

Kittisares²,

Sanguansri³

et al. 2022

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…[113] In general, because there are fewer parameters to tune, GRUs are able to offer comparable performance to LSTMs while being significantly faster to compute, [114] though both LSTMs and GRUs have proven effective for a wide variety of applications, including EMG analysis; because they can act on "memory", they have been used to analyze and classify time-series signals, including EMG signals. [115]…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Intelligent Systems for Muscle Tracking: A Review on Sensor‐Algorithm Synergy

Putcha

Nguyen

Smith

et al. 2023

Advanced Intelligent Systems

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Advanced technologies for muscle tracking provide easy access to identify and track muscle activity, often for the purposes of therapeutic interventions. The necessity of muscle trackers arises from the acute and chronic sources that disrupt neuromuscular control, resulting in an impaired ability to perform daily activities without assistance. In the context of human–machine interfaces, muscle trackers can serve as the “sensory” component, providing real‐time information to machines, such as exoskeletons and prosthetics, that can act upon such information for therapeutic and functional aid. Recently developed devices for muscle tracking rely on combinations of sensor modalities and algorithms to extract information from biosignals track muscle activity, or even extrapolate kinematic information, including gestures. However, a number of obstacles remain to be overcome to further facilitate the practical implementation of muscle‐tracking technologies, the most notable being real‐time analysis of biosignal data and extracting kinematic information from complex movements. This review attempts to cover the mechanisms behind various sensor modalities and algorithms commonly used for muscle tracking, as well as establish the current state of applications within the field. Given its multidisciplinary nature and ability to free users from rehabilitation constraints, the field of muscle tracking holds significant promise for future study.

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“…The advance in deep learning methods such as Long short-term memory (LSTM) recurrent neural networks seems promising to detect temporal muscle activity [30]. Anyway, every approach algorithm will require extensive retraining, cross-validating, and the addition of social factors that may improve the model's accuracy and feasibility.…”

Section: Strength Limitation and The Further Implicationmentioning

confidence: 99%

Classification of elderly pain severity from automated video clip facial action units analysis: A study from a Thai data repository

Gomutbutra

Kittisares

Sanguansri

et al. 2022

Preprint

View full text Add to dashboard Cite

Data from 255 Thais with chronic pain were collected at Chiang Mai Medical School Hospital. After the patients selfrated their level of pain, a smartphone camera was used to capture faces for 10 s at a one-meter distance. For those unable to self-rate, a video recording was taken immediately after the move that causes the pain. The trained assistant rated each video clip for the pain assessment in advanced dementia (PAINAD). The pain was classi ed into three levels: mild, moderate, and severe. OpenFace© was used to convert the video clips into 18 facial action units (FAUs). Five classi cation models were used, including logistic regression, multilayer perception, naïve Bayes, decision tree, k-nearest neighbors (KNN), and support vector machine (SVM). Out of the models that only used FAU described in the literature (FAU 4,

show abstract

Long short-term memory (LSTM) recurrent neural network for muscle activity detection

Cited by 29 publications

References 58 publications

Classification of elderly pain severity from automated video clip facial action unit analysis: A study from a Thai data repository

Classification of elderly pain severity from automated video clip facial action unit analysis: A study from a Thai data repository

Intelligent Systems for Muscle Tracking: A Review on Sensor‐Algorithm Synergy

Classification of elderly pain severity from automated video clip facial action units analysis: A study from a Thai data repository

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