Detection of movement onset using EMG signals for upper-limb exoskeletons in reaching tasks.
Emilio Trigili, Lorenzo Grazi, Simona Crea and 5 others
PMID 30922326WHAT IT FOUND
In 10 healthy people, EMG detectors caught forward reaching starts 81.1% of the time and avoided false starts 96.2% of the time.
Return-to-rest detection was poorer, catching 60.9% of starts.
Key findings
01The anterior deltoid detector caught 81.1% of forward reaching movement starts.
02The same detector avoided false starts in 96.2% of non-start samples.
03For return-to-rest movement, the extensor carpi ulnaris detector caught 60.9% of movement starts.
STILL TO COME
How it was doneWhat they foundWhat it means for OTs
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What it does not show
10 healthy subjects were tested, so the results do not show how the detector works in people with severe arm disability or abnormal EMG patterns. The detector was evaluated offline, not used to control the exoskeleton in real time during patient movement. Return-to-rest detection had low sensitivity, which the authors state would make real-time retraction difficult. The return-to-rest start condition held the arm extended and restrained it, leaving residual muscle activation that increased background noise in the EMG signals. The paper discusses possible use for people with severe arm disabilities but does not report patient outcomes, safety, usability, or functional improvement.
Declared interests
The paper lists funding from Regione Toscana and H2020 LEIT Information and Communication Technologies. The supplied text does not include a conflict-of-interest statement.
The easy way to misread this
Do not conclude that EMG-controlled exoskeletons can reliably assist patients with arm disability. The study tested 10 healthy people offline, and the best return-to-rest detector caught only 60.9% of movement starts.