Hybrid Control Interface of a Semi-soft Assistive Glove for People with Spinal Cord Injuries.

Active assistive devices have been designed to augment the hand grasping capabilities of individuals with spinal cord injuries (SCI). An intuitive bio-signal of wrist extension has been utilized in the device control, which imitates the passive grasping effect of tenodesis. However, controlling these devices in this manner limits the wrist joint motion while grasping. This paper presents a novel hybrid control interface and corresponding algorithms (i.e., a hybrid control method) of the Semi-soft Assistive Glove (SAG) developed for individuals with C6/C7-SCI. The secondary control interface is implemented to enable/disable the grasp trigger signal generated by the primary interface detecting the wrist extension. A simulation study reveals that the hybrid control method can facilitate grasping situations faced in daily activities. Empirical results with three healthy subjects suggest that the proposed method can assist the user to reach and grasp objects with the SAG naturally.

Estimating Sit-to-Stand Dynamics Using a Single Depth Camera.

Kinetic and dynamic motion analysis provides quantitative, functional assessments of human ability that are unobtainable through static imaging methods or subjective surveys. While biomechanics facilities are equipped to perform this measurement and analysis, the clinical translation of these methods is limited by the specialized skills and equipment needed. This paper presents and validates a method for estimating dynamic effects such as joint torques and body momenta using a single depth camera. An allometrically scaled, sagittal plane dynamic model is used to estimate the joint torques at the ankles, knees, hips, and low back, as well as the torso momenta, and shear and normal loads at the L5-S1 disk. These dynamic metrics are applied to the sit-to-stand motion and validated against a gold-standard biomechanical system consisting of full-body active motion-capture and force-sensing systems. The metrics obtained from the proposed method were found to have excellent concordance with peak metrics that are consistent with prior biomechanical studies. This suggests the feasibility of using this system for rapid clinical assessment, with applications in diagnostics, longitudinal tracking, and quantifying patient recovery.

Kinematic and Kinetic Validation of an Improved Depth Camera Motion Assessment System Using Rigid Bodies.

The study of joint kinematics and dynamics has broad clinical applications, including the identification of pathological motions or compensation strategies and the analysis of dynamic stability. High-end motion capture systems, however, are expensive and require dedicated camera spaces with lengthy setup and data processing commitments. Depth cameras, such as the Microsoft Kinect, provide an inexpensive, marker-free alternative at the sacrifice of joint-position accuracy. In this work, we present a fast framework for adding biomechanical constraints to the joint estimates provided by a depth camera system. We also present a new model for the lower lumbar joint angle. We validate key joint position, angle, and velocity measurements against a gold standard active motion-capture system on ten healthy subjects performing sit to stand (STS). Our method showed significant improvement in mean absolute error and intraclass correlation coefficients for the recovered joint angles and position-based metrics. These improvements suggest that depth cameras can provide an accurate and clinically viable method of rapidly assessing the kinematics and kinetics of the STS action, providing data for further analysis using biomechanical or machine learning methods.

Tracking Kinematic and Kinetic Measures of Sit to Stand using an Instrumented Spine Orthosis.

Age related spinal deformity is becoming an increasingly prevalent problem, resulting in decreased quality of life. While spinal deformity can be corrected via surgical intervention, a large number of people with spinal fusions require follow-up surgery due to further degeneration. The identification of changes to a subjects kinematics and kinetics post-surgery are limited by a lack of methods to collect patient specific motion data over the course of surgical recovery. This paper introduces an Instrumented Spine Orthosis (ISO) that can capture the motions of the subjects torso without requiring the use of a control computer or other dedicated motion capture equipment. This system is used to collect the peak torso angles and velocities for a single subject performing sit-to-stand actions. The accuracy of the ISO is evaluated using motion capture, during different sit-to-stand protocols designed to highlight motion changes that have been seen in subjects with reduced mobility. This system was found to provide reliable measurements of these kinematic and kinetic torso measures across all tested motions, demonstrating the potential for the use of Instrumented Spine Orthotics to provide quantitative measures during the surgical recovery process.

A functional method for generating individualized spine models from motion-capture data.

A representative model is necessary for the analysis of spine kinematics and dynamics during motion. Existing models, based on stationary imaging or cadaveric data, may not be accurate through the full range of spinal motion or for clinical populations. In this paper, we propose a functional method for estimating subject-specific spinal joint centers, generating a one-joint or two-joint kinematic model of the spine. These models are driven by the motion of the thorax and pelvis as observed by eight surface landmarks. We apply this method to experimental data from ten subjects performing flexion/extension and sit-to-stand motions. The recovered functional models are assessed against an allometric model though the analysis of marker residuals. We found that the functional models provide lower residuals than the allometric methods. Between the functional models, the two-joint model provided lower residuals with less sensitivity to the training action, while the one-joint model should be trained on the motion of interest.

A sEMG Classification Framework with Less Training Data.

Supervised machine learning algorithms, such as Artificial Neural Network (ANN), have been applied to surface electromyograph (sEMG) to classify user's muscular states. This paper introduces a novel framework to design a binary sEMG classifier to distinguish if the user performs a repetitive motion with a dumbbell. This framework enables to reduce the number of tasks required for collecting training data as it utilizes prior knowledge of sEMG. The performance of the proposed classifier is validated experimentally. Experimental results show that the proposed framework enables the design of a classifier which distinguishes the user's state with a 95.7% success rate. This accuracy is comparable to an accuracy of ANN classifier (99.6%), but with less training data. Under the identical training conditions, the accuracy of the proposed framework outperforms the ANN classifier whose accuracy drops to 65.6%.

Fusing motion-capture and inertial measurements for improved joint state recovery: An application for sit-to-stand actions.

The estimate of joint angles, velocities, and accelerations is a key component of biomechanical modelling. The literature presents a variety of sensing modalities and algorithms to recover the full joint state, with tuning parameters varying between different applications, actions, and limbs. Comparisons between these methods are frequently limited to angles only, without comparison between the joint velocities and accelerations. This paper introduces an algorithm to fuse motion-capture and inertial measurements to recover the full state during a sit-to-stand task. This algorithm is then compared to three other methods: Kalman filtering on motion-capture or inertial measurements alone and the standard angular recovery/differentiation method. It is shown that the fusion of both optical and inertial measurements reduce the ripple and offset artefacts which become pronounced in high acceleration human motions.

Generating physically realistic kinematic and dynamic models from small data sets: An application for sit-to-stand actions.

Kinematic and dynamic models are used to create simplified, yet accurate representations of reality. In application to biological systems, there is often a choice on what level of complexity is appropriate for the model. This paper introduces a structured method for obtaining an accurate model that can represent the sit-to-stand motion and reproduce the associated contact forces in the standing phase. These models are generated from small datasets, just five measured sit-to-stand actions, and result in simple, physically realisable dynamic models. The assumptions made apriori on the model are minimal, with the number of segments, axes of rotation, marker allocation and location, and dynamic model all determined from this small dataset. From this initial analysis, the use of a triple pendulum with a simple point mass at the centre of the torso was found to be representative. Through the generation of these simple, repeatable models, this work aims to develop a modelling framework that is suitable for the study of biological systems and clinical use.

Initial investigation into the effect of an Active/Passive exoskeleton on hammer curl performance in healthy subjects.

Assistive robotic devices are traditionally constrained by their power source. Entirely passive devices exist, but are limited by their fixed mechanical parameters. This work introduces a new device that can provide active and passive assistance. This device provides assistance in a passive mode, but retains the actively change this passive response. This paper examines the effect different passive parameter settings have on healthy subjects performing hammer curls. Passive parameter settings to either increase or decrease the number of curls a subject could perform were found. An average increase of 84% or a decrease of 33% in curls was produced by varying the passive parameters. These effects were seen across all six subjects. This indicates that there is potential for the Active/Passive framework to provide lightweight, energy efficient assistance.