Timing-dependent effects of transcranial direct current stimulation with mirror therapy on daily function and motor control in chronic stroke: a randomized controlled pilot study

Background

The timing of transcranial direct current stimulation (tDCS) with neurorehabilitation interventions may affect its modulatory effects. Motor function has been reported to be modulated by the timing of tDCS; however, whether the timing of tDCS would also affect restoration of daily function and upper extremity motor control with neurorehabilitation in stroke patients remains largely unexplored. Mirror therapy (MT) is a potentially effective neurorehabilitation approach for improving paretic arm function in stroke patients. This study aimed to determine whether the timing of tDCS with MT would influence treatment effects on daily function, motor function and motor control in individuals with chronic stroke.

Methods

This study was a double-blinded randomized controlled trial. Twenty-eight individuals with chronic stroke received one of the following three interventions: (1) sequentially combined tDCS with MT (SEQ), (2) concurrently combined tDCS with MT (CON), and (3) sham tDCS with MT (SHAM). Participants received interventions for 90 min/day, 5 days/week for 4 weeks. Daily function was assessed using the Nottingham Extended Activities of Daily Living Scale. Upper extremity motor function was assessed using the Fugl-Meyer Assessment Scale. Upper extremity motor control was evaluated using movement kinematic assessments.

Results

There were significant differences in daily function between the three groups. The SEQ group had greater improvement in daily function than the CON and SHAM groups. Kinematic analyses showed that movement time of the paretic hand significantly reduced in the SEQ group after interventions. All three groups had significant improvement in motor function from pre-intervention to post-intervention.

Conclusion

The timing of tDCS with MT may influence restoration of daily function and movement efficiency of the paretic hand in chronic stroke patients. Sequentially applying tDCS prior to MT seems to be advantageous for enhancing daily function and hand movement control, and may be considered as a potentially useful strategy in future clinical application.

Trial registration

ClinicalTrials.gov Identifier: NCT02827864. Registered on 29th June, 2016.

Detection of driver manual distraction via image-based hand and ear recognition

Driving distraction is a leading cause of fatal car accidents, and almost nine people are killed in the US each day because of distracting activities. Therefore, reducing the number of distraction-affected traffic accidents remains an imperative issue. A novel algorithm for detection of drivers' manual distraction was proposed in this manuscript. The detection algorithm consists of two modules. The first module predicts the bounding boxes of the driver's right hand and right ear from RGB images. The second module takes the bounding boxes as input and predicts the type of distraction. 106,677 frames extracted from videos, which were collected from twenty participants in a driving simulator, were used for training (50%) and testing (50%). For distraction classification, the results indicated that the proposed framework could detect normal driving, using the touchscreen, and talking with a phone with F1-score 0.84, 0.69, 0.82, respectively. For overall distraction detection, it achieved F1-score of 0.74. The whole framework ran at 28 frames per second. The algorithm achieved comparable overall accuracy with similar research, and was more efficient than other methods. A demo video for the algorithm can be found at https://youtu.be/NKclK1bHRd4.

Algorithmically detectable directional changes in upper extremity motion indicate substantial myoelectric shoulder muscle fatigue during a repetitive manual task

Repetitive workplace tasks are associated with fatigue-induced changes to shoulder muscular strategies, potentially altering kinematics and elevating susceptibility to tissue overexposures. Accessible and reliable methods to detect shoulder muscle fatigue in the workplace are therefore valuable. Detectable changes in joint motion may provide a plausible fatigue identification method. In this investigation, the onset of the first kinematic changes, as identified by a symbolic motion representation (SMSR) algorithm, and the onset of substantial surface electromyography (sEMG) mean power frequency (MPF) fatigue were not significantly different, both occurring around 10% of task duration. This highlights the potential utility of SMSR identified directional changes in joint motion during repetitive tasks as a cue of substantial muscle fatigue, enabling ergonomics responses that can mitigate shoulder muscular fatigue accumulation and its associated deleterious physical effects. Practitioner Summary: The onset of substantial muscle fatigue during a repetitive dynamic task was assessed using kinematics and myoelectric-based techniques. Algorithmically detectable directional changes in upper extremity joint motion occurred with the onset of substantial muscle fatigue, highlighting the potential of this as a useful approach for workplace fatigue identification.

The reachable 3-D workspace volume is a measure of payload and body-mass-index: A quasi-static kinetic assessment

An experimental protocol with five tasks is proposed for a low-cost empirical assessment of the reachable 3-D workspace (RWS), including both close-to-torso and far-from-torso regions. Ten participants repeated the protocol for four distinct hand payloads. The RWS expressed as a point cloud and its non-convex alpha-shape were obtained for each case. Moreover, individual strength surrogates for glenohumeral flexion and abduction, and elbow flexion were collected using a dynamometer. The RWS volume was statistically modelled using payload, body-mass-index and the strength surrogates as predictors. For increasing payload, a significant (r = -0.736,p < 0.001) decrease in RWS volume was found for distinct payload cases across all subjects. The only significant predictors found for the RWS volume were normalized payload (F = 73.740,p < 0.001) and body-mass-index (F = 11.008,p = 0.003). No significant interactions were found. The consequent regression model (F(2,27) = 41.11, p < 0.001, R_adj² = 0.7345) explained around 73% of the variation in the data. The RWS volume is a function of payload and body-mass-index.