Feasibility of using a depth camera or pressure mat for visual feedback balance training with functional electrical stimulation.

Individuals with incomplete spinal-cord injury/disease are at an increased risk of falling due to their impaired ability to maintain balance. Our research group has developed a closed-loop visual-feedback balance training (VFBT) system coupled with functional electrical stimulation (FES) for rehabilitation of standing balance (FES + VFBT system); however, clinical usage of this system is limited by the use of force plates, which are expensive and not easily accessible. This study aimed to investigate the feasibility of a more affordable and accessible sensor such as a depth camera or pressure mat in place of the force plate. Ten able-bodied participants (7 males, 3 females) performed three sets of four different standing balance exercises using the FES + VFBT system with the force plate. A depth camera and pressure mat collected centre of mass and centre of pressure data passively, respectively. The depth camera showed higher Pearson's correlation (r > 98) and lower root mean squared error (RMSE < 10 mm) than the pressure mat (r > 0.82; RMSE < 4.5 mm) when compared with the force plate overall. Stimulation based on the depth camera showed lower RMSE than that based on the pressure mat relative to the FES + VFBT system. The depth camera shows potential as a replacement sensor to the force plate for providing feedback to the FES + VFBT system.

Contribution of Each Motor Point of Quadriceps Femoris to Knee Extension Torque During Neuromuscular Electrical Stimulation.

Transcutaneous neuromuscular electrical stimulation (NMES) can be used to activate the quadriceps femoris muscle to produce knee extension torque via seven distinct motor points, defined as the most sensitive locations on the muscle belly to electrical stimuli. However, it remains unclear how much individual motor points of the quadriceps femoris muscle contribute to the knee joint torque. Here we systematically investigated the contribution of each motor point of the quadriceps femoris muscle to the knee joint torque produced by paired electrical stimuli. Ten able-bodied individuals participated in this study. Paired electrical stimuli was applied by delivering electrical impulses on the motor points in all combinations among seven motor points (i.e., totaling to 127 combinations) at two different stimulation intensities (i.e., 25% and 50% of the maximum) while recording isometric knee joint torque. The contribution of individual motor points was estimated using statistical analyses. We found that a linear addition of twitch torques induced by single motor point stimulus overestimated the twitch torques induced by multiple motor point stimulations, suggesting overlaps in muscle fibres activated by each motor point. Using multiple linear regressions, we identified the average contribution of each motor point to the knee extension torque during paired electrical stimuli and found significant differences between these torque contributions. We demonstrated that seven distinct motor points can be activated for the quadriceps muscle group using paired electrical stimuli and identified the contribution of each motor point to knee extension torque during twitch muscle contraction; these findings provide useful information to design rehabilitation using NMES on quadriceps femoris muscles.

A New Dataset for Facial Motion Analysis in Individuals With Neurological Disorders.

We present the first public dataset with videos of oro-facial gestures performed by individuals with oro-facial impairment due to neurological disorders, such as amyotrophic lateral sclerosis (ALS) and stroke. Perceptual clinical scores from trained clinicians are provided as metadata. Manual annotation of facial landmarks is also provided for a subset of over 3300 frames. Through extensive experiments with multiple facial landmark detection algorithms, including state-of-the-art convolutional neural network (CNN) models, we demonstrated the presence of bias in the landmark localization accuracy of pre-trained face alignment approaches in our participant groups. The pre-trained models produced higher errors in the two clinical groups compared to age-matched healthy control subjects. We also investigated how this bias changes when the existing models are fine-tuned using data from the target population. The release of this dataset aims to propel the development of face alignment algorithms robust to the presence of oro-facial impairment, support the automatic analysis and recognition of oro-facial gestures, enhance the automatic identification of neurological diseases, as well as the estimation of disease severity from videos and images.

Customized Access Technology for Children using Head Movement Recognition.

Children with cerebral palsy and complex communication needs face limitations in their access technology (AT) usage. Speech recognition software and conventional ATs (e.g., mechanical switches) can be insufficient for those with speech impairment and limited control of voluntary motion. Automatic recognition of head movements represents a promising pathway. Previous studies have shown the robustness of head pose estimation algorithms on adult participants, but further research is needed to use these methods with children. An algorithm for head movement recognition was implemented and evaluated on videos recorded in a naturalistic environment when children were playing a videogame. A face-tracking algorithm was used to detect the main facial landmarks. Head poses were then estimated using the Pose from Orthography and Scaling with Iterations (POSIT) algorithm and three head movements were classified through Hidden Markov Models (HMMs). Preliminary classification results obtained from the analysis of videos of five typically developing children showed an accuracy of up to 95.6% in predicting head movements.