A Pilot Feasibility Trial of an Upper Extremity Assistive System

Objective

To develop and clinically evaluate a customizable active upper extremity (UE) assistive system with integrated functional electrical stimulation (FES) that improves function and independence of individuals during activities of daily living (ADLs).

Design

Single-arm, prospective, open-label cohort feasibility trial.

Setting

An academic research institution.

Participants

Subjects were 5 adults with a medical history of stroke resulting in distal UE impairment (N=5). The subjects volunteered from recruitment materials that detailed information about the study.

Interventions

A novel, wearable, lightweight, low-profile, and patient-tailored UE assistive system. It comprises a splint component and FES unit that may each be controlled by electromyography (EMG) signals, inertial measurement units (IMUs), manual control source (joystick), and/or voice control.

Main Outcome Measures

Several occupational therapy outcome measures were used, including the Canadian Occupational Performance Measure (COPM), Action Research Arm Test (ARAT), The Box and Blocks Test (BBT), the ABILHAND-Manual Ability Measure, and Patient Reported Outcomes Measurement Information System (PROMIS) UE Short Form.

Results

All participants learned to use our UE assistive system to perform ADLs and were able to use it independently at home. Most participants experienced a clinically meaningful improvement in both performance and satisfaction for the majority of their COPM goals while using the system. All participants experienced improvement in hand grip and release as shown by their baseline and post assessment scores for hand function (BBT, ARAT) and patient-reported outcomes (ABILHAND, PROMIS).

Conclusions

The clinical outcomes suggest that our UE assistive system improves functional performance in patients with UE impairment, allowing them to engage more actively in ADLs. Further innovation including elbow and shoulder components will allow users to have more degrees of freedom during tasks.

A Scoping Review of the Characteristics of Activity-based Therapy Interventions Across the Continuum of Care for People Living With Spinal Cord Injury or Disease

Objective

To identify the characteristics of activity-based therapy (ABT) that individuals with spinal cord injury and disease (SCI/D) participate in across the continuum of care.

Data Sources

A search of 8 databases was conducted from inception to 4 March 2020: Medline, CINAHL, Embase, Emcare, PEDro, APA PsycINFO, Cochrane Database of Systematic Reviews, and the CENTRAL. The search strategy used terms identifying the population (SCI/D) and concept (ABT).

Study Selection

Original studies involving individuals with SCI/D ≥16 years of age participating in ABT interventions for >1 session were included in the review. The Joanna Briggs Institute guidelines for scoping reviews were followed. The initial search produced 2306 records. Title, abstract, and full-text screening by 2 independent reviewers yielded 140 articles.

Data Extraction

Data extraction was conducted by 3 independent reviewers and charted according to key themes. Data fields included participant demographics, ABT interventions, exercises, parameters, technology, and setting. Data synthesis included frequency counts and descriptive analysis of key themes.

Data Synthesis

Eighty percent of participants were male. Eighty-seven percent of studies included individuals with tetraplegia (26% exclusive). Fifty-six percent of studies occurred in a research lab. Fifty-four percent of studies were single modality interventions encompassing the whole body (71%). Sixteen main types of ABT exercises were identified. The most common were treadmill training (59%), muscle strengthening (36%), and overground walking (33%). Electrical stimulation (50%) and virtual reality (6%) were used in combination with an ABT exercise. Eighty-four types of parameters were identified. Six were general intervention parameters and 78 were specific to the type of ABT exercise. Sixteen main categories of technology were reported. The most common were motorized treadmills (47%) and transcutaneous electrical stimulation (44%).

Conclusions

The characteristics of ABT are diverse in scope. The results will inform the content to include in tools that track ABT participation and performance.

Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions

Brain-Computer Interface (BCI) is a technology that uses electroencephalographic (EEG) signals to control external devices, such as Functional Electrical Stimulation (FES). Visual BCI paradigms based on P300 and Steady State Visually Evoked potentials (SSVEP) have shown high potential for clinical purposes. Numerous studies have been published on P300- and SSVEP-based non-invasive BCIs, but many of them present two shortcomings: (1) they are not aimed for motor rehabilitation applications, and (2) they do not report in detail the artificial intelligence (AI) methods used for classification, or their performance metrics. To address this gap, in this paper the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was applied to prepare a systematic literature review (SLR). Papers older than 10 years, repeated or not related to a motor rehabilitation application, were excluded. Of all the studies, 51.02% referred to theoretical analysis of classification algorithms. Of the remaining, 28.48% were for spelling, 12.73% for diverse applications (control of wheelchair or home appliances), and only 7.77% were focused on motor rehabilitation. After the inclusion and exclusion criteria were applied and quality screening was performed, 34 articles were selected. Of them, 26.47% used the P300 and 55.8% the SSVEP signal. Five applications categories were established: Rehabilitation Systems (17.64%), Virtual Reality environments (23.52%), FES (17.64%), Orthosis (29.41%), and Prosthesis (11.76%). Of all the works, only four performed tests with patients. The most reported machine learning (ML) algorithms used for classification were linear discriminant analysis (LDA) (48.64%) and support vector machine (16.21%), while only one study used a deep learning algorithm: a Convolutional Neural Network (CNN). The reported accuracy ranged from 38.02 to 100%, and the Information Transfer Rate from 1.55 to 49.25 bits per minute. While LDA is still the most used AI algorithm, CNN has shown promising results, but due to their high technical implementation requirements, many researchers do not justify its implementation as worthwile. To achieve quick and accurate online BCIs for motor rehabilitation applications, future works on SSVEP-, P300-based and hybrid BCIs should focus on optimizing the visual stimulation module and the training stage of ML and DL algorithms.

Highlighting gaps in spinal cord injury research in activity-based interventions for the upper extremity: A scoping review

BACKGROUND

Upper extremity activity-based therapy for neurologic disorders employs high-intensity, high repetition functional training to exploit neuroplasticity and improve function. Research focused on high-intensity upper extremity activity-based therapy for persons with spinal cord injury (SCI) is limited.

OBJECTIVE

To summarize high-intensity activity-based interventions used in neurological disorders for their current or potential application to SCI.

METHODS

The scoping review included articles from MEDLINE, CINAHL, Cochrane CENTRAL, and OTSeeker with the criteria: non-invasive activity-based interventions delivered at least three times/week for two weeks, upper extremity functional outcomes, age 13 years or older, English language, and neurological disorders three months post onset/injury.

RESULTS

The search yielded 172 studies. There were seven studies with SCI, all in adults. Activity-based interventions in SCI included task-specific training and gaming, with and without electrical stimulation, and a robotic exoskeleton. The other populations found in the review included studies in stroke, cerebral palsy, and multiple sclerosis. Thirty-four different interventions were reported in other populations. In comparison to the extensive stroke research, work in SCI was not found for high-intensity interventions using virtual reality, brain stimulation, rehabilitation devices, and applications to the home and telerehab settings.

CONCLUSION

The results highlight critical gaps within upper extremity high-intensity activity-based research in SCI.