Motor Control Insights on Walking Planner and its Stability

The application of biomechanic and motor control models in the control of bidedal robots (humanoids, and exoskeletons) has revealed limitations of our understanding of human locomotion. A recently proposed model uses the potential energy for bipedal structures to model the bipedal dynamics, and it allows to predict the system dynamics from its kinematics. This work proposes a task-space planner for human-like straight locomotion that target application of in rehabilitation robotics and computational neuroscience. The proposed architecture is based on the potential energy model and employs locomotor strategies from human data as a reference for human behaviour. The model generates Centre of Mass (CoM) trajectories, foot swing trajectories and the Base of Support (BoS) over time. The data show that the proposed architecture can generate behaviour in line with human walking strategies for both the CoM and the foot swing. Despite the CoM vertical trajectory being not as smooth as a human trajectory, yet the proposed model significantly reduces the error in the estimation of the CoM vertical trajectory compared to the inverted pendulum models. The proposed model is also able to asses the stability based on the body kinematics embedding in currently used in the clinical practice. However, the model also implies a shift in the interpretation of the spatiotemporal parameters of the gait, which are now determined by the conditions for the equilibrium and not \textit{vice versa}. In other words, locomotion is a dynamic reaching where the motor primitives are also determined by gravity

AUTOMATED MULTI-FEATURE SEGMENTATION OF TREADMILL RUNNING

The definition of gait events and phases have been well established in the literature through the use of qualitative movement descriptors. The repeatable, objective definitions of gait events and phases is the cornersone of sucess when performin a multi-center trial. A correlation-based multi-feature automated segmentation algorithm was developed and applied to treadmill running data. The features used were soley from 3D kinematic marker trajectory data, including generated features such as vectors between kinematic markers. The algorithm was compared against a trained tester who used visual inspection and threshold limits of the vGRF to segment stance. The automated segmentation approach was shown to consistently identify the same gait events as the trained tester, representing a significant time savings for the signal processing of large volume treadmill running data

Graceful User Following for Mobile Balance Assistive Robot in Daily Activities Assistance

Numerous diseases and aging can cause degeneration of people's balance ability resulting in limited mobility and even high risks of fall. Robotic technologies can provide more intensive rehabilitation exercises or be used as assistive devices to compensate for balance ability. However, With the new healthcare paradigm shifting from hospital care to home care, there is a gap in robotic systems that can provide care at home. This paper introduces Mobile Robotic Balance Assistant (MRBA), a compact and cost-effective balance assistive robot that can provide both rehabilitation training and activities of daily living (ADLs) assistance at home. A three degrees of freedom (3-DoF) robotic arm was designed to mimic the therapist arm function to provide balance assistance to the user. To minimize the interference to users' natural pelvis movements and gait patterns, the robot must have a Human-Robot Interface(HRI) that can detect user intention accurately and follow the user's movement smoothly and timely. Thus, a graceful user following control rule was proposed. The overall control architecture consists of two parts: an observer for human inputs estimation and an LQR-based controller with disturbance rejection. The proposed controller is validated in high-fidelity simulation with actual human trajectories, and the results successfully show the effectiveness of the method in different walking modes

RECOMMENDATIONS FOR MINIMUM TRIAL NUMBERS DURING WALKING GAIT

In a rehabilitation context, athletes may not be able to complete large numbers of trials during testing due to joint edema and pain. The purpose of this research was to determine the minimum number of trials needed to achieve a negligibly fluctuating temporal variance profile during walking gait. The time-series kinematics of the hip, knee and ankle were recorded from 10 participants, completing 11 trials each. The time-series variance of each kinematic variables were calculated for ten trials and used as a reference. Using a two-sample SPM1D {t} (α=0.05), all variance combinations (9, 8, 7, ... 3 of 11 trials) from the same participants were compared to the reference. Results showed a minimum of 7 trials were needed to achieve ’stable‘ kinematic variance during walking gait. This study provides evidence for selecting an appropriate number of walking trials in gait analysis, especially in early-stage rehabilitation for patients with joint pain or edema