ArticleJournal of neuroengineering and rehabilitation2018
Identification of forearm skin zones with similar muscle activation patterns during activities of daily living.
Article in Journal of neuroengineering and rehabilitation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- A Systematic Review of EMG Applications for the Characterization of Forearm and Hand Muscle Activity during Activities of Daily Living: Results, Challenges, and Open Issues.Sensors (Basel, Switzerland) · 2021Pooled it
- Automated assessment of upper limb spasticity in stroke patients with fusion of multichannel surface electromyography features.Journal of rehabilitation medicine · 2025Article
- Multimodal Deep Learning Model for Cylindrical Grasp Prediction Using Surface Electromyography and Contextual Data During Reaching.Biomimetics (Basel, Switzerland) · 2025Article
- Electromyography Parameters to Discriminate Hand Osteoarthritis and Infer Their Functional Impact.Sensors (Basel, Switzerland) · 2024Article
- Electromyography and kinematics data of the hand in activities of daily living with special interest for ergonomics.Scientific data · 2023Article
- Toward Early and Objective Hand Osteoarthritis Detection by Using EMG during Grasps.Sensors (Basel, Switzerland) · 2023Article
- Deep learning and session-specific rapid recalibration for dynamic hand gesture recognition from EMG.Frontiers in bioengineering and biotechnology · 2022Article
- A three-dimensional musculoskeletal model of the dog.Scientific reports · 2021Article
- A calibrated database of kinematics and EMG of the forearm and hand during activities of daily living.Scientific data · 2019Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA deeper knowledge of the activity of the forearm muscles during activities of daily living (ADL) could help to better understand the role of those muscles and allow clinicians to treat motor dysfunctions more effectively and thus improve patients' ability to perform activities of daily living.
methodsIn this work, we recorded sEMG activity from 30 spots distributed over the skin of the whole forearm of six subjects during the performance of 21 representative simulated ADL from the Sollerman Hand Function Test. Functional principal component analysis and hierarchical cluster analysis (HCA) were used to identify forearm spots with similar muscle activation patterns.
resultsThe best classification of spots with similar activity in simulated ADL consisted in seven muscular-anatomically coherent groups: (1) wrist flexion and ulnar deviation; (2) wrist flexion and radial deviation; (3) digit flexion; (4) thumb extension and abduction/adduction; (5) finger extension; (6) wrist extension and ulnar deviation; and (7) wrist extension and radial deviation.
conclusionThe number of sEMG sensors could be reduced from 30 to 7 without losing any relevant information, using them as representative spots of the muscular activity of the forearm in simulated ADL. This may help to assess muscle function in rehabilitation while also simplifying the complexity of prosthesis control.
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