Decoding hand and wrist movement intention from chronic stroke survivors with hemiparesis using a user-friendly, wearable EMG-based neural interface

Meyers, Eric C.; Gabrieli, David; Tacca, Nick; Wengerd, Lauren; Darrow, Michael; Schlink, Bryan R.; Baumgart, Ian; Friedenberg, David A.

doi:10.1186/s12984-023-01301-w

Cited by 2 publications

(4 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Seven individuals (3 female, 4 male; 60 ± 5 years) with a history of stroke and hemiparesis participated in the study. EMG was recorded from their affected arm using the NeuroLife ® EMG sleeve (figures 1(A) and (B)) [22] as they attempted various hand movements. An additional dataset of various movements and grasps was recorded from seven able-bodied (4 female, 3 male; 27 ± 1 years) individuals to serve as a benchmark for comparison of HD-EMG features.…”

Section: Study Participantsmentioning

confidence: 99%

“…The methods used in this experiment are similar to those previously described by Meyers et al [22]. Participants were seated comfortably at a table with a computer monitor in front of them and both arms resting (figure 1(C)).…”

Section: Experimental Setup and Paradigmmentioning

confidence: 99%

“…Both stroke and able-bodied participants attempted the following 12 movements: Hand Close, Hand Open, Pointing Index, Thumb Flexion, Thumb Extension, Thumb Abduction, Wrist Supination, Wrist Pronation, Wrist Flexion, Wrist Extension, Thumb Two Point Pinch, and Key Pinch (supplementary figure 7). These movements were identified by an experienced occupational therapist and have been used in previous studies [22,24] due to their relevance for dexterous hand use in functional tasks. The movements are also included in standardized clinical assessments such as the UEFM and ARAT.…”

Section: Experimental Setup and Paradigmmentioning

confidence: 99%

“…EMG data were recorded at a sampling rate of 3,000 Hz using an Intan Recording Controller (Intan Technologies, Los Angeles, CA). Pre-processing consisted of notch filtering the data at 60 Hz and bandpass filtering between 20 and 400 Hz with a 10th order Butterworth filter [22,25]. Prior to EMG feature extraction, additional artifact filtering steps were performed to correct artifacts due to sleeve shift, electrode lifting, and impedance changes over time.…”

Section: Emg Signal Processingmentioning

confidence: 99%

See 3 more Smart Citations

Identifying alterations in hand movement coordination from chronic stroke survivors using a wearable high-density EMG sleeve

Tacca,

Baumgart,

Schlink

et al. 2024

J. Neural Eng.

View full text Add to dashboard Cite

Non-invasive, high-density electromyography (HD-EMG) has emerged as a useful tool to collect a range of neurophysiological motor information. Recent studies have demonstrated changes in EMG features that occur after stroke, which correlate with functional ability, highlighting their potential use as biomarkers. However, previous studies have largely explored these EMG features in isolation with individual electrodes to assess gross movements, limiting their potential clinical utility. Here, able-bodied (N=7) and chronic stroke subjects (N=7) performed 12 functional hand and wrist movements while HD-EMG was recorded using a wearable sleeve. We demonstrate that a variety of HD-EMG features, or views, can be decomposed from the wearable sleeve. Stroke subjects, on average, had higher co-contraction and reduced muscle coupling when attempting to open their hand and actuate their thumb. In an expanded dataset consisting of 37 movements, we characterized muscle synergies in the forearm of able-bodied individuals. We found that the high-density array provides additional resolution over manually placed electrodes, which may help dissociate finer nuances in motor control. Additionally, muscle synergies decomposed in the stroke population were relatively preserved, with a large spatial overlap in composition of matched synergies. Alterations in synergy composition demonstrated reduced coupling between digit extensors and muscles that actuate the thumb, as well as an increase in flexor activity in the stroke group. Average synergy activations during movements revealed differences in coordination, highlighting overactivation of antagonist muscles and compensatory strategies. When combining co-contraction and muscle synergy features, the first principal component was correlated with upper-extremity Fugl Meyer hand sub-score of stroke participants (R2=0.86). Principal component embeddings of individual features revealed interpretable measures of motor coordination and muscle coupling alterations. These results demonstrate the feasibility of predicting motor function through features decomposed from a wearable HD-EMG sleeve, which could be leveraged to improve stroke research and clinical care.

show abstract

Section: Study Participantsmentioning

confidence: 99%

Section: Experimental Setup and Paradigmmentioning

confidence: 99%

Section: Experimental Setup and Paradigmmentioning

confidence: 99%

Section: Emg Signal Processingmentioning

confidence: 99%

See 2 more Smart Citations

Identifying alterations in hand movement coordination from chronic stroke survivors using a wearable high-density EMG sleeve

Tacca,

Baumgart,

Schlink

et al. 2024

J. Neural Eng.

View full text Add to dashboard Cite

show abstract

Wearable high-density EMG sleeve for complex hand gesture classification and continuous joint angle estimation

Tacca,

Dunlap,

Donegan

et al. 2024

Sci Rep

View full text Add to dashboard Cite

High-density electromyography (HD-EMG) can provide a natural interface to enhance human–computer interaction (HCI). This study aims to demonstrate the capability of a novel HD-EMG forearm sleeve equipped with up to 150 electrodes to capture high-resolution muscle activity, decode complex hand gestures, and estimate continuous hand position via joint angle predictions. Ten able-bodied participants performed 37 hand movements and grasps while EMG was recorded using the HD-EMG sleeve. Simultaneously, an 18-sensor motion capture glove calculated 23 joint angles from the hand and fingers across all movements for training regression models. For classifying across the 37 gestures, our decoding algorithm was able to differentiate between sequential movements with $$97.3 \pm 0.3\%$$ 97.3 ± 0.3 % accuracy calculated on a 100 ms bin-by-bin basis. In a separate mixed dataset consisting of 19 movements randomly interspersed, decoding performance achieved an average bin-wise accuracy of $$92.8 \pm 0.8\%$$ 92.8 ± 0.8 % . When evaluating decoders for use in real-time scenarios, we found that decoders can reliably decode both movements and movement transitions, achieving an average accuracy of $$93.3 \pm 0.9\%$$ 93.3 ± 0.9 % on the sequential set and $$88.5 \pm 0.9\%$$ 88.5 ± 0.9 % on the mixed set. Furthermore, we estimated continuous joint angles from the EMG sleeve data, achieving a $$R^2$$ R 2 of $$0.884 \pm 0.003$$ 0.884 ± 0.003 in the sequential set and $$0.750 \pm 0.008$$ 0.750 ± 0.008 in the mixed set. Median absolute error (MAE) was kept below 10° across all joints, with a grand average MAE of $$1.8 \pm 0.04^\circ$$ 1.8 ± 0 . 04 ∘ and $$3.4 \pm 0.07^\circ$$ 3.4 ± 0 . 07 ∘ for the sequential and mixed datasets, respectively. We also assessed two algorithm modifications to address specific challenges for EMG-driven HCI applications. To minimize decoder latency, we used a method that accounts for reaction time by dynamically shifting cue labels in time. To reduce training requirements, we show that pretraining models with historical data provided an increase in decoding performance compared with models that were not pretrained when reducing the in-session training data to only one attempt of each movement. The HD-EMG sleeve, combined with sophisticated machine learning algorithms, can be a powerful tool for hand gesture recognition and joint angle estimation. This technology holds significant promise for applications in HCI, such as prosthetics, assistive technology, rehabilitation, and human–robot collaboration.

show abstract

Decoding hand and wrist movement intention from chronic stroke survivors with hemiparesis using a user-friendly, wearable EMG-based neural interface

Cited by 2 publications

References 43 publications

Identifying alterations in hand movement coordination from chronic stroke survivors using a wearable high-density EMG sleeve

Identifying alterations in hand movement coordination from chronic stroke survivors using a wearable high-density EMG sleeve

Wearable high-density EMG sleeve for complex hand gesture classification and continuous joint angle estimation

Contact Info

Product

Resources

About