Measuring functional hand use in children with unilateral cerebral palsy using accelerometry and machine learning.

AIM: To investigate wearable sensors for measuring functional hand use in children with unilateral cerebral palsy (CP). METHOD: Dual wrist-worn accelerometry data were collected from three females and seven males with unilateral CP (mean age = 10 years 2 months [SD 3 years]) while performing hand tasks during video-recorded play sessions. Video observers labelled instances of functional and non-functional hand use. Machine learning was compared to the conventional activity count approach for identifying unilateral hand movements as functional or non-functional. Correlation and agreement analyses compared the functional usage metrics derived from each method. RESULTS: The best-performing machine learning approach had high precision and recall when trained on an individual basis (F1 = 0.896 [SD 0.043]). On an individual basis, the best-performing classifier showed a significant correlation (r = 0.990, p < 0.001) and strong agreement (bias = 0.57%, 95% confidence interval = -4.98 to 6.13) with video observations. When validated in a leave-one-subject-out scenario, performance decreased significantly (F1 = 0.584 [SD 0.076]). The activity count approach failed to detect significant differences in non-functional or functional hand activity and showed no significant correlation or agreement with the video observations. INTERPRETATION: With further development, wearable accelerometry combined with machine learning may enable quantitative monitoring of everyday functional hand use in children with unilateral CP.

Selective peripheral nerve recording using simulated human median nerve activity and convolutional neural networks.

BACKGROUND: It is difficult to create intuitive methods of controlling prosthetic limbs, often resulting in abandonment. Peripheral nerve interfaces can be used to convert motor intent into commands to a prosthesis. The Extraneural Spatiotemporal Compound Action Potentials Extraction Network (ESCAPE-NET) is a convolutional neural network (CNN) that has previously been demonstrated to be effective at discriminating neural sources in rat sciatic nerves. ESCAPE-NET was designed to operate using data from multi-channel nerve cuff arrays, and use the resulting spatiotemporal signatures to classify individual naturally evoked compound action potentials (nCAPs) based on differing source fascicles. The applicability of this approach to larger and more complex nerves is not well understood. To support future translation to humans, the objective of this study was to characterize the performance of this approach in a computational model of the human median nerve. METHODS: Using a cross-sectional immunohistochemistry image of a human median nerve, a finite-element model was generated and used to simulate extraneural recordings. ESCAPE-NET was used to classify nCAPs based on source location, for varying numbers of sources and noise levels. The performance of ESCAPE-NET was also compared to ResNet-50 and MobileNet-V2 in the context of classifying human nerve cuff data. RESULTS: Classification accuracy was found to be inversely related to the number of nCAP sources in ESCAPE-NET (3-class: 97.8% ± 0.1%; 10-class: 89.3% ± 5.4% in low-noise conditions, 3-class: 70.3% ± 0.1%; 10-class: 52.5% ± 0.3% in high-noise conditions). ESCAPE-NET overall outperformed both MobileNet-V2 (3-class: 96.5% ± 1.1%; 10-class: 84.9% ± 1.7% in low-noise conditions, 3-class: 86.0% ± 0.6%; 10-class: 41.4% ± 0.9% in high-noise conditions) and ResNet-50 (3-class: 71.2% ± 18.6%; 10-class: 40.1% ± 22.5% in low-noise conditions, 3-class: 81.3% ± 4.4%; 10-class: 31.9% ± 4.4% in high-noise conditions). CONCLUSION: All three networks were found to learn to differentiate nCAPs from different sources, as evidenced by performance levels well above chance in all cases. ESCAPE-NET was found to have the most robust performance, despite decreasing performance as the number of classes increased, and as noise was varied. These results provide valuable translational guidelines for designing neural interfaces for human use.

Manipulating facial musculature with functional electrical stimulation as an intervention for major depressive disorder: a focused search of literature for a proposal.

BACKGROUND: Major Depressive Disorder (MDD) is associated with interoceptive deficits expressed throughout the body, particularly the facial musculature. According to the facial feedback hypothesis, afferent feedback from the facial muscles suffices to alter the emotional experience. Thus, manipulating the facial muscles could provide a new "mind-body" intervention for MDD. This article provides a conceptual overview of functional electrical stimulation (FES), a novel neuromodulation-based treatment modality that can be potentially used in the treatment of disorders of disrupted brain connectivity, such as MDD. METHODS: A focused literature search was performed for clinical studies of FES as a modulatory treatment for mood symptoms. The literature is reviewed in a narrative format, integrating theories of emotion, facial expression, and MDD. RESULTS: A rich body of literature on FES supports the notion that peripheral muscle manipulation in patients with stroke or spinal cord injury may enhance central neuroplasticity, restoring lost sensorimotor function. These neuroplastic effects suggest that FES may be a promising innovative intervention for psychiatric disorders of disrupted brain connectivity, such as MDD. Recent pilot data on repetitive FES applied to the facial muscles in healthy participants and patients with MDD show early promise, suggesting that FES may attenuate the negative interoceptive bias associated with MDD by enhancing positive facial feedback. Neurobiologically, the amygdala and nodes of the emotion-to-motor transformation loop may serve as potential neural targets for facial FES in MDD, as they integrate proprioceptive and interoceptive inputs from muscles of facial expression and fine-tune their motor output in line with socio-emotional context. CONCLUSIONS: Manipulating facial muscles may represent a mechanistically novel treatment strategy for MDD and other disorders of disrupted brain connectivity that is worthy of investigation in phase II/III trials.

Development of priorities for a Canadian strategy to advance activity-based therapies after spinal cord injury.

STUDY DESIGN: Participatory design. OBJECTIVES: Activity-based therapies (ABT) have physical and psychosocial benefits for individuals with spinal cord injury (SCI). A Canadian ABT summit was held to: (1) identify methods used in stroke rehabilitation that may be appropriate for SCI; (2) understand the current state of ABT activities in Canada; and (3) identify priorities for ABT research and care for the next five years. SETTING: Stakeholder-engaged meeting at a tertiary rehabilitation hospital. METHODS: Thirty-nine stakeholders, including individuals with SCI, frontline clinicians, healthcare administrators, researchers, funders and health policy experts, attended. Two participants were note-takers. Priority identification occurred through input from stakeholder groups, followed by individual voting. Conventional content analysis was used to synthesize the information in the meeting notes. RESULTS: The strengths of ABT in stroke rehabilitation included clear and clinically feasible definitions, measurements and interventions, and recognized requirements for implementation (e.g. behavior change, partnerships). Knowledge gaps concerning ABT activities in Canada were identified for acute and community settings, non-traumatic populations, and the interventions, equipment and standardized measures (i.e. upper limb, activity levels) used. Five priorities for ABT across the continuum of care were identified: (1) Identify current ABT activities; (2) Create a network to facilitate dialog; (3) Track engagement in ABT activities; (4) Develop and implement best practice recommendations; and (5) Study optimal timing, methods, and dose of ABT. Working groups were formed to address priorities 1-3. CONCLUSIONS: The priorities will guide SCI research and care activities in Canada over the next five years. SPONSORSHIP: Praxis Spinal Cord Institute.

Properties of the surface electromyogram following traumatic spinal cord injury: a scoping review.

Traumatic spinal cord injury (SCI) disrupts spinal and supraspinal pathways, and this process is reflected in changes in surface electromyography (sEMG). sEMG is an informative complement to current clinical testing and can capture the residual motor command in great detail-including in muscles below the level of injury with seemingly absent motor activities. In this comprehensive review, we sought to describe how the sEMG properties are changed after SCI. We conducted a systematic literature search followed by a narrative review focusing on sEMG analysis techniques and signal properties post-SCI. We found that early reports were mostly focused on the qualitative analysis of sEMG patterns and evolved to semi-quantitative scores and a more detailed amplitude-based quantification. Nonetheless, recent studies are still constrained to an amplitude-based analysis of the sEMG, and there are opportunities to more broadly characterize the time- and frequency-domain properties of the signal as well as to take fuller advantage of high-density EMG techniques. We recommend the incorporation of a broader range of signal properties into the neurophysiological assessment post-SCI and the development of a greater understanding of the relation between these sEMG properties and underlying physiology. Enhanced sEMG analysis could contribute to a more complete description of the effects of SCI on upper and lower motor neuron function and their interactions, and also assist in understanding the mechanisms of change following neuromodulation or exercise therapy.

Compensation Strategies for Bioelectric Signal Changes in Chronic Selective Nerve Cuff Recordings: A Simulation Study.

Peripheral nerve interfaces (PNIs) allow us to extract motor, sensory, and autonomic information from the nervous system and use it as control signals in neuroprosthetic and neuromodulation applications. Recent efforts have aimed to improve the recording selectivity of PNIs, including by using spatiotemporal patterns from multi-contact nerve cuff electrodes as input to a convolutional neural network (CNN). Before such a methodology can be translated to humans, its performance in chronic implantation scenarios must be evaluated. In this simulation study, approaches were evaluated for maintaining selective recording performance in the presence of two chronic implantation challenges: the growth of encapsulation tissue and rotation of the nerve cuff electrode. Performance over time was examined in three conditions: training the CNN at baseline only, supervised re-training with explicitly labeled data at periodic intervals, and a semi-supervised self-learning approach. This study demonstrated that a selective recording algorithm trained at baseline will likely fail over time due to changes in signal characteristics resulting from the chronic challenges. Results further showed that periodically recalibrating the selective recording algorithm could maintain its performance over time, and that a self-learning approach has the potential to reduce the frequency of recalibration.

Functional electrical stimulation of the facial muscles to improve symptoms in individuals with major depressive disorder: pilot feasibility study.

BACKGROUND: Currently, the mainstay of treatment in patients diagnosed with major depressive disorder (MDD) requiring medical attention is second generation anti-depressants. However, about 40% of patients treated with second-generation anti-depressants do not respond to initial treatment and approximately 70% do not achieve remission during the first-step treatment. There are a few non-pharmacological options available, but none have shown consistently positive results. There is a need for an intervention that is relatively easy to administer, produces consistently positive results and is associated with minimal side effects. In the current study, we assessed the feasibility of using transcutaneous Functional Electrical Stimulation Therapy (FEST) of the facial muscles, as a tool for improving depressive symptoms in individuals with MDD. RESULTS: Ten (10) individuals with moderate to severe MDD received three FEST sessions/week for a minimum of 10 to a maximum of 40 sessions. All study participants completed the required 10 therapy sessions, and 5 of the 10 participants completed additional 30 (totalling 40) FEST sessions. There were no adverse events or concerns regarding compliance to therapy. We found statistically significant improvements on Hamilton Rating Scale for Depression (HDS) and Inventory of Depressive Symptomatology (IDS) measures. However, no significant improvements were found on Positive and Negative Affect Scale and 10-point Visual Analogue Scale scales. Participants reported improvements in sleeping patterns, and this correlated with statistically significant improvements on sleep parameters of HDS and IDS measures. CONCLUSION: This study indicates that facial FEST is an acceptable, practical, and safe treatment in individuals with MDD. We provide preliminary evidence to show improvements in depressive symptoms following a minimum of 10 FEST sessions.

Egocentric video: a new tool for capturing hand use of individuals with spinal cord injury at home.

BACKGROUND: Current upper extremity outcome measures for persons with cervical spinal cord injury (cSCI) lack the ability to directly collect quantitative information in home and community environments. A wearable first-person (egocentric) camera system is presented that aims to monitor functional hand use outside of clinical settings. METHODS: The system is based on computer vision algorithms that detect the hand, segment the hand outline, distinguish the user's left or right hand, and detect functional interactions of the hand with objects during activities of daily living. The algorithm was evaluated using egocentric video recordings from 9 participants with cSCI, obtained in a home simulation laboratory. The system produces a binary hand-object interaction decision for each video frame, based on features reflecting motion cues of the hand, hand shape and colour characteristics of the scene. RESULTS: The output from the algorithm was compared with a manual labelling of the video, yielding F1-scores of 0.74 ± 0.15 for the left hand and 0.73 ± 0.15 for the right hand. From the resulting frame-by-frame binary data, functional hand use measures were extracted: the amount of total interaction as a percentage of testing time, the average duration of interactions in seconds, and the number of interactions per hour. Moderate and significant correlations were found when comparing these output measures to the results of the manual labelling, with ρ = 0.40, 0.54 and 0.55 respectively. CONCLUSIONS: These results demonstrate the potential of a wearable egocentric camera for capturing quantitative measures of hand use at home.

Considerations and recommendations for selection and utilization of upper extremity clinical outcome assessments in human spinal cord injury trials.

STUDY DESIGN: This is a focused review article. OBJECTIVES: This review presents important features of clinical outcomes assessments (COAs) in human spinal cord injury research. Considerations for COAs by trial phase and International Classification of Functioning, Disability and Health are presented as well as strengths and recommendations for upper extremity COAs for research. Clinical trial tools and designs to address recruitment challenges are identified. METHODS: The methods include a summary of topics discussed during a two-day workshop, conceptual discussion of upper extremity COAs and additional focused literature review. RESULTS: COAs must be appropriate to trial phase and particularly in mid-late-phase trials, should reflect recovery vs. compensation, as well as being clinically meaningful. The impact and extent of upper vs. lower motoneuron disease should be considered, as this may affect how an individual may respond to a given therapeutic. For trials with broad inclusion criteria, the content of COAs should cover all severities and levels of SCI. Specific measures to assess upper extremity function as well as more comprehensive COAs are under development. In addition to appropriate use of COAs, methods to increase recruitment, such as adaptive trial designs and prognostic modeling to prospectively stratify heterogeneous populations into appropriate cohorts should be considered. CONCLUSIONS: With an increasing number of clinical trials focusing on improving upper extremity function, it is essential to consider a range of factors when choosing a COA. SPONSORS: Craig H. Neilsen Foundation, Spinal Cord Outcomes Partnership Endeavor.

Rehabilitation technologies and interventions for individuals with spinal cord injury: translational potential of current trends.

In the past, neurorehabilitation for individuals with neurological damage, such as spinal cord injury (SCI), was focused on learning compensatory movements to regain function. Presently, the focus of neurorehabilitation has shifted to functional neurorecovery, or the restoration of function through repetitive movement training of the affected limbs. Technologies, such as robotic devices and electrical stimulation, are being developed to facilitate repetitive motor training; however, their implementation into mainstream clinical practice has not been realized. In this commentary, we examined how current SCI rehabilitation research aligns with the potential for clinical implementation. We completed an environmental scan of studies in progress that investigate a physical intervention promoting functional neurorecovery. We identified emerging interventions among the SCI population, and evaluated the strengths and gaps of the current direction of SCI rehabilitation research. Seventy-three study postings were retrieved through website and database searching. Study objectives, outcome measures, participant characteristics and the mode(s) of intervention being studied were extracted from the postings. The FAME (Feasibility, Appropriateness, Meaningfulness, Effectiveness, Economic Evidence) Framework was used to evaluate the strengths and gaps of the research with respect to likelihood of clinical implementation. Strengths included aspects of Feasibility, as the research was practical, aspects of Appropriateness as the research aligned with current scientific literature on motor learning, and Effectiveness, as all trials aimed to evaluate the effect of an intervention on a clinical outcome. Aspects of Feasibility were also identified as a gap; with two thirds of the studies examining emerging technologies, the likelihood of successful clinical implementation was questionable. As the interventions being studied may not align with the preferences of clinicians and priorities of patients, the Appropriateness of these interventions for the current health care environment was questioned. Meaningfulness and Economic Evidence were also identified as gaps since few studies included measures reflecting the perceptions of the participants or economic factors, respectively. The identified gaps will likely impede the clinical uptake of many of the interventions currently being studied. Future research may lessen these gaps through a staged approach to the consideration of the FAME elements as novel interventions and technologies are developed, evaluated and implemented.

Short-term neuroplastic effects of brain-controlled and muscle-controlled electrical stimulation.

OBJECTIVES: Functional electrical stimulation (FES) has been shown to facilitate the recovery of grasping function in individuals with incomplete spinal cord injury. Neurophysiological theory suggests that this benefit may be further enhanced by a more consistent pairing of the voluntary commands sent from the user's brain down their spinal cord with the electrical stimuli applied to the user's periphery. The objective of the study was to compare brain-machine interfaces (BMIs)-controlled and electromyogram (EMG)-controlled FES therapy to three more well-researched therapies, namely, push button-controlled FES therapy, voluntary grasping (VOL), and BMI-guided voluntary grasping. MATERIALS AND METHODS: Ten able-bodied participants underwent one hour of each of five grasping training modalities, including BMI-controlled FES (BMI-FES), EMG-controlled FES (EMG-FES), conventional push button-controlled FES, VOL, and BMI-guided voluntary grasping. Assessments, including motor-evoked potential, grip force, and maximum voluntary contraction, were conducted immediately before and after each training period. RESULTS: Motor-evoked potential-based outcome measures were more upregulated following BMI-FES and especially EMG-FES than they were following VOL or FES. No significant changes were found in the more functional outcome measures. CONCLUSIONS: These results provide preliminary evidence suggesting the potential of BMI-FES and EMG-FES to induce greater neuroplastic changes than conventional therapies, although the precise mechanism behind these changes remains speculative. Further investigation will be required to elucidate the underlying mechanisms and to conclusively determine whether these effects can translate into better long-term functional outcomes and quality of life for individuals with spinal cord injury.

Neuromodulation of emotion using functional electrical stimulation applied to facial muscles.

BACKGROUND AND OBJECTIVE: Major depressive disorder (MDD) is a common condition for which available pharmaceutical treatments are not always effective and can have side-effects. Therefore, alternative and/or complementary MDD treatments are needed. Research on facial expressions has shown that facial movements can induce the corresponding emotions, particularly when specific attention is paid to voluntarily activating muscles that are typically only activated involuntarily while expressing emotions. We hypothesized that functional electrical stimulation (FES) applied to facial muscles may enhance this effect due to its ability to modulate central nervous system plasticity. Thus, applying FES to the facial muscles associated with smiling (including the "Duchenne marker") may increase the activity of subcortical nuclei related to positive emotions and counteract symptoms of depression. METHODS: Twelve able-bodied subjects received FES and were compared with a group of 12 control subjects. Both groups underwent the same experimental procedures involving a cognitive task, and a deception was used such that subjects were unaware that the objective was to modulate mood. Assessments with the Positive and Negative Affect Schedule-Expanded Form (PANAS-X) were administered before and after the experiment. RESULTS: No significant between-group differences were found in the change scores for our primary outcomes, the PANAS-X item "happy," and aggregate scores "Joviality" and "Positive Affect." Significant differences were, however, detected for secondary outcomes "determined," "daring," "scared," and "concentrating." CONCLUSIONS: These results suggest that modulating emotion using FES may be possible, but is difficult to target accurately. Further work is warranted to explore FES applications to MDD.

Time series classification of multi-channel nerve cuff recordings using deep learning.

Neurostimulation and neural recording are crucial to develop neuroprostheses that can restore function to individuals living with disabilities. While neurostimulation has been successfully translated into clinical use for several applications, it remains challenging to robustly collect and interpret neural recordings, especially for chronic applications. Nerve cuff electrodes offer a viable option for recording nerve signals, with long-term implantation success. However, nerve cuff electrodes' signals have low signal-to-noise ratios, resulting in reduced selectivity between neural pathways. The objective of this study was to determine whether deep learning techniques, specifically networks tailored for time series applications, can increase the recording selectivity achievable using multi-contact nerve cuff electrodes. We compared several neural network architectures, the impact and trade-off of window length on classification performance, and the benefit of data augmentation. Evaluation was carried out using a previously collected dataset of 56-channel nerve cuff recordings from the sciatic nerve of Long-Evans rats, which included afferent signals evoked using three types of mechanical stimuli. Through this study, the best model achieved an accuracy of 0.936 ± 0.084 and an F1-score of 0.917 ± 0.103, using 50 ms windows of data and an augmented training set. These results demonstrate the effectiveness of applying CNNs designed for time-series data to peripheral nerve recordings, and provide insights into the relationship between window duration and classification performance in this application.

Resource-Efficient Neural Network Architectures for Classifying Nerve Cuff Recordings on Implantable Devices.

BACKGROUND: Closed-loop functional electrical stimulation can use recorded nerve signals to create implantable systems that make decisions regarding nerve stimulation in real-time. Previous work demonstrated convolutional neural network (CNN) discrimination of activity from different neural pathways recorded by a high-density multi-contact nerve cuff electrode, achieving state-of-the-art performance but requiring too much data storage and power for a practical implementation on surgically implanted hardware. OBJECTIVE: To reduce resource utilization for an implantable implementation, with minimal performance loss for CNNs that can discriminate between neural pathways in multi-contact cuff electrode recordings. METHODS: Neural networks (NNs) were evaluated using rat sciatic nerve recordings previously collected using 56-channel cuff electrodes to capture spatiotemporal neural activity patterns. NNs were trained to classify individual, natural compound action potentials (nCAPs) elicited by sensory stimuli. Three architectures were explored: the previously reported ESCAPE-NET, a fully convolutional network, and a recurrent neural network. Variations of each architecture were evaluated based on F1-score, number of weights, and floating-point operations (FLOPs). RESULTS: NNs were identified that, when compared to ESCAPE-NET, require 1,132-1,787x fewer weights, 389-995x less memory, and 6-11,073x fewer FLOPs, while maintaining macro F1-scores of 0.70-0.71 compared to a baseline of 0.75. Memory requirements range from 22.69 KB to 58.11 KB, falling within on-chip memory sizes from published deep learning accelerators fabricated in ASIC technology. CONCLUSION: Reduced versions of ESCAPE-NET require significantly fewer resources without significant accuracy loss, thus can be more easily incorporated into a surgically implantable device that performs closed-loop responsive neural stimulation.

Hand Grasp Classification in Egocentric Video After Cervical Spinal Cord Injury.

OBJECTIVE: The hand function of individuals with spinal cord injury (SCI) plays a crucial role in their independence and quality of life. Wearable cameras provide an opportunity to analyze hand function in non-clinical environments. Summarizing the video data and documenting dominant hand grasps and their usage frequency would allow clinicians to quickly and precisely analyze hand function. METHOD: We introduce a new hierarchical model to summarize the grasping strategies of individuals with SCI at home. The first level classifies hand-object interaction using hand-object contact estimation. We developed a new deep model in the second level by incorporating hand postures and hand-object contact points using contextual information. RESULTS: In the first hierarchical level, a mean of 86% ±1.0% was achieved among 17 participants. At the grasp classification level, the mean average accuracy was 66.2 ±12.9%. The grasp classifier's performance was highly dependent on the participants, with accuracy varying from 41% to 78%. The highest grasp classification accuracy was obtained for the model with smoothed grasp classification, using a ResNet50 backbone architecture for the contextual head and a temporal pose head. DISCUSSION: We introduce a novel algorithm that, for the first time, enables clinicians to analyze the quantity and type of hand movements in individuals with spinal cord injury at home. The algorithm can find applications in other research fields, including robotics, and most neurological diseases that affect hand function, notably, stroke and Parkinson's.

Development of an activity-based therapy tracking tool: Item generation and reduction using the Delphi method.

CONTEXT/OBJECTIVE: Activity-based therapies (ABT) are increasingly used in rehabilitation after spinal cord injury or disease (SCI/D). However, the absence of standardized tools to track the details of an ABT program hinders the collection of data needed for client-tailored programming and resource allocation. The objective of this study is to determine the content to include in an ABT tracking tool for people living with SCI/D. DESIGN: Cross-sectional e-survey. SETTING: Community. PARTICIPANTS: The 60 participants from Canada and the United States who had knowledge and/or experience with ABT included: individuals with SCI/D; hospital clinicians (i.e. physical and occupational therapists/assistants); community-based clinicians; hospital or community clinic administrators; researchers; and funders, advocates and policy makers. INTERVENTIONS: None. OUTCOME MEASURES: A Delphi e-survey comprised 16 types of ABT (e.g. treadmill training) and 4 types of technology (e.g. virtual reality). Participants rated the importance of including each item on a tracking tool and the feasibility to track each item using a 9-point Likert scale. RESULTS: After two survey rounds, nine types of ABT and one technology were identified as important to include in a tracking tool. All items rated as important were considered feasible for clinicians and people with SCI/D to track, except crawling. CONCLUSION: This study identified the types of ABT and technology to include in an ABT tracking tool. Such a tool may provide details of an ABT program that can support decision-making at the individual, program and health system levels and aid the development of best practice guidelines.

Modulations in neural pathways excitability post transcutaneous spinal cord stimulation among individuals with spinal cord injury: a systematic review.

Introduction: Transcutaneous spinal cord stimulation (TSCS), a non-invasive form of spinal cord stimulation, has been shown to improve motor function in individuals living with spinal cord injury (SCI). However, the effects of different types of TSCS currents including direct current (DC-TSCS), alternating current (AC-TSCS), and spinal paired stimulation on the excitability of neural pathways have not been systematically investigated. The objective of this systematic review was to determine the effects of TSCS on the excitability of neural pathways in adults with non-progressive SCI at any level. Methods: The following databases were searched from their inception until June 2022: MEDLINE ALL, Embase, Web of Science, Cochrane Library, and clinical trials. A total of 4,431 abstracts were screened, and 23 articles were included. Results: Nineteen studies used TSCS at the thoracolumbar enlargement for lower limb rehabilitation (gait & balance) and four studies used cervical TSCS for upper limb rehabilitation. Sixteen studies measured spinal excitability by reporting different outcomes including Hoffmann reflex (H-reflex), flexion reflex excitability, spinal motor evoked potentials (SMEPs), cervicomedullay evoked potentials (CMEPs), and cutaneous-input-evoked muscle response. Seven studies measured corticospinal excitability using motor evoked potentials (MEPs) induced by transcranial magnetic stimulation (TMS), and one study measured somatosensory evoked potentials (SSEPs) following TSCS. Our findings indicated a decrease in the amplitude of H-reflex and long latency flexion reflex following AC-TSCS, alongside an increase in the amplitudes of SMEPs and CMEPs. Moreover, the application of the TSCS-TMS paired associative technique resulted in spinal reflex inhibition, manifested by reduced amplitudes in both the H-reflex and flexion reflex arc. In terms of corticospinal excitability, findings from 5 studies demonstrated an increase in the amplitude of MEPs linked to lower limb muscles following DC-TSCS, in addition to paired associative stimulation involving repetitive TMS on the brain and DC-TSCS on the spine. There was an observed improvement in the latency of SSEPs in a single study. Notably, the overall quality of evidence, assessed by the modified Downs and Black Quality assessment, was deemed poor. Discussion: This review unveils the systematic evidence supporting the potential of TSCS in reshaping both spinal and supraspinal neuronal circuitries post-SCI. Yet, it underscores the critical necessity for more rigorous, high-quality investigations.

Closed-Loop Control of Functional Electrical Stimulation Using a Selectively Recording and Bidirectional Nerve Cuff Interface.

Discriminating recorded afferent neural information can provide sensory feedback for closed-loop control of functional electrical stimulation, which restores movement to paralyzed limbs. Previous work achieved state-of-the-art off-line classification of electrical activity in different neural pathways recorded by a multi-contact nerve cuff electrode, by applying deep learning to spatiotemporal neural patterns. The objective of this study was to demonstrate the feasibility of this approach in the context of closed-loop stimulation. Acute in vivo experiments were conducted on 11 Long Evans rats to demonstrate closed-loop stimulation. A 64-channel ( 8×8 ) nerve cuff electrode was implanted on each rat's sciatic nerve for recording and stimulation. A convolutional neural network (CNN) was trained with spatiotemporal signal recordings associated with 3 different states of the hindpaw (dorsiflexion, plantarflexion, and pricking of the heel). After training, firing rates were reconstructed from the classifier outputs for each of the three target classes. A rule-based closed-loop controller was implemented to produce ankle movement trajectories using neural stimulation, based on the classified nerve recordings. Closed-loop stimulation was successfully demonstrated in 6 subjects. The number of successful movement sequence trials per subject ranged from 1-17 and number of correct state transitions per trial ranged from 3-53. This work demonstrates that a CNN applied to multi-contact nerve cuff recordings can be used for closed-loop control of functional electrical stimulation.

Tracking activity-based therapy for people living with spinal cord injury or disease: insights gained through focus group interviews with key stakeholders.

PURPOSE: The development of a tool to track participation in activity-based therapy (ABT) for people with spinal cord injury or disease (SCI/D) was identified as a priority of the Canadian ABT Community of Practice. The objective of this study was to understand multi-stakeholder perspectives on tracking ABT participation across the continuum of care. MATERIALS AND METHODS: Forty-eight individuals from six stakeholder groups (persons living with SCI/D; hospital therapists; community trainers; administrators; researchers; and funders, advocates and policy experts) were recruited to participate in focus group interviews. Participants were asked open-ended questions concerning the importance of and parameters around tracking ABT. Transcripts were analyzed using conventional content analysis. RESULTS: Themes reflected the Who, What, Where, When, Why and How of tracking ABT. Participants described the importance of involving hospital therapists, community trainers and individuals with SCI/D in tracking ABT to capture both subjective and objective parameters across the continuum of care and injury trajectory. Digital tracking tools were favoured, although paper-based versions were regarded as a necessity in some circumstances. CONCLUSIONS: Findings highlighted the importance of tracking ABT participation for individuals with SCI/D. The information may guide the development of ABT practice guidelines and support the implementation of ABT in Canada.

Tracking the details of activity-based therapy (ABT) sessions and programs across the continuum of care and injury trajectory may provide important information to support the development of ABT practice guidelines and implementation strategies.Tracking objective and subjective parameters are needed to provide a comprehensive description of an ABT session and program.Clinicians and individuals with spinal cord injury or disease (SCI/D) should both be able to track ABT to accommodate all settings and types of data.Digital tracking tools, such as an app, may provide an accessible, versatile and efficient way of tracking ABT.

Hand contour detection in wearable camera video using an adaptive histogram region of interest.

BACKGROUND: Monitoring hand function at home is needed to better evaluate the effectiveness of rehabilitation interventions. Our objective is to develop wearable computer vision systems for hand function monitoring. The specific aim of this study is to develop an algorithm that can identify hand contours in video from a wearable camera that records the user's point of view, without the need for markers. METHODS: The two-step image processing approach for each frame consists of: (1) Detecting a hand in the image, and choosing one seed point that lies within the hand. This step is based on a priori models of skin colour. (2) Identifying the contour of the region containing the seed point. This is accomplished by adaptively determining, for each frame, the region within a colour histogram that corresponds to hand colours, and backprojecting the image using the reduced histogram. RESULTS: In four test videos relevant to activities of daily living, the hand detector classification accuracy was 88.3%. The contour detection results were compared to manually traced contours in 97 test frames, and the median F-score was 0.86. CONCLUSION: This algorithm will form the basis for a wearable computer-vision system that can monitor and log the interactions of the hand with its environment.