We tackle the problem of tracking the human lower body as an initial step toward an automatic motion assessment system for clinical mobility evaluation, using a multimodal system that combines Inertial Measurement Unit (IMU) data, RGB images, and point cloud depth measurements. This system applies the factor graph representation to an optimization problem that provides 3-D skeleton joint estimations. In this paper, we focus on improving the temporal consistency of the estimated human trajectories to greatly extend the range of operability of the depth sensor. More specifically, we introduce a new factor graph factor based on Koopman theory that embeds the nonlinear dynamics of several lower-limb movement activities. This factor performs a two-step process: first, a custom activity recognition module based on spatial temporal graph convolutional networks recognizes the walking activity; then, a Koopman pose prediction of the subsequent skeleton is used as an a priori estimation to drive the optimization problem toward more consistent results. We tested the performance of this module a dataset composed of multiple clinical lower-limb mobility tests, and we show that our approach reduces outliers on the skeleton form by almost 1 m, while preserving natural walking trajectories at depths up to more than 10 m.

We propose a novel system to track human lower-body motion as part of a larger movement assessment system for clinical evaluation. Our system combines multiple wearable Inertial Measurement Unit (IMU) sensors and a single external RGB-D camera. We use a factor graph with a Sliding Window Filter (SWF) formulation that merges 2-D joint data extracted from the RGB images via a Deep Neural Network, raw depth information, raw IMU gyroscope readings, and estimated foot contacts extracted from IMU gyroscope and accelerometer data. For the system, we use an articulated model of human body motion based on differential manifolds. We compare the results of our system against a gold-standard motion capture system and a vision-only alternative. Our proposed system qualitatively presents smoother 3D joint trajectories when compared to noisy depth data, allowing for more realistic gait estimations. At the same time, with respect to the vision-only baseline, it improves the median of the joint trajectories by around 2cm, while considerably reducing outliers by up to 0.6m.

Exosuits can reduce metabolic demand and improve gait. Controllers explicitly derived from biological mechanisms that reflect the user's joint or muscle dynamics should in theory allow for individualized assistance and enable adaptation to changing gait. With the goal of developing an exosuit control strategy based on muscle power, we present an approach for estimating, at real time rates, when the soleus muscle begins to generate positive power. A low-profile ultrasound system recorded B-mode images of the soleus in walking individuals. An automated routine using optical flow segmented the data to a normalized gait cycle and estimated the onset of concentric contraction at real-time rates (~130Hz). Segmentation error was within 1% of the gait cycle compared to using ground reaction forces. Estimation of onset of concentric contraction had a high correlation (R2=0.92) and an RMSE of 2.6% gait cycle relative to manual estimation. We demonstrated the ability to estimate the onset of concentric contraction during fixed speed walking in healthy individuals that ranged from 39.3% to 45.8% of the gait cycle and feasibility in two persons post-stroke walking at comfortable walking speed. We also showed the ability to measure a shift in onset timing to 7% earlier when the biological system adapts from level to incline walking. Finally, we provided an initial evaluation for how the onset of concentric contraction might be used to inform exosuit control in level and incline walking.

Hemiparetic gait after stroke is typically asymmetric and energetically inefficient. A major contributor to walking deficits is impaired paretic ankle function. Impaired paretic ankle plantarflexion (PF) reduces forward propulsion symmetry and impaired paretic ankle dorsiflexion (DF) diminishes ground clearance during swing. We have developed soft wearable robots (soft exosuits) to assist paretic PF and DF during walking after stroke. Through experimental studies with poststroke patients, we have demonstrated that exosuits can improve forward propulsion symmetry and ground clearance in walking, ultimately reducing the metabolic cost of walking. This paper presents an optimized soft exosuit aimed at use in clinical gait training for patients poststroke. The optimized exosuit is lightweight, easy to don and doff, and capable of efficiently delivering mechanical assistance to the paretic ankle. This paper focuses on the optimized controller that can deliver well-timed consistent ankle assistance to patients. A preliminary study was performed using this exosuit with three poststroke patients with heterogeneous gait patterns. Results showed that compared to a previously published controller, more consistent assistive force profiles could be delivered to individuals poststroke while consuming 50% less electrical power. Additionally, a preliminary biomechanical assessment was performed during overground walking.