PhD student, National University of Singapore / Advanced Robotic Centre (Singapore)


Development of an inertial sensor-based stride parameters estimation system from continuous gait

Functional mobility typically involves the ability to ambulate safely even when using assistive devices. With an ageing population that continues to grow, it is estimated that by 2030, one in every five Singaporeans will be aged 65 and above. With ageing, frailty significantly increases the risk of falls, disability, long term care and death. Therefore, a comprehensive and measurable gait analysis able to provide evidence of frequent geriatric syndromes such as falls, dementia and frailty is urgently needed.

Different applications of wearable sensor-based systems for gait monitoring and rehabilitation have been proposed to mitigate the deteriorating motor functions that affects the elderly. In light of functional recovery of the elderly, lower limb functions of the ageing are known to be preferentially altered by age due to the weakening of the lower limb muscle functions. Traditional training effectively counteracts some of these functional declines, but is not always accessible due to its cost, accessibility, and incapacities of some seniors to practice some exercises. The combination of a mobile gait analysis system and neuromuscular electrical stimulation has arisen as a promising approach to aid in gait restoration, combining technologies that could provide an alternative to traditional practices. Electrical muscle stimulation has shown to improve functional and molecular muscle physiology which leads to a better gait and balance performance especially among less active elderly, while the mobile gait monitoring system closes the loop between the residual physical capabilities of the user and the neuro-prosthesis.

In our research, we developed a wearable system which provides accurate, reproducible, clinically gait relevant data during uninterrupted walking. To aid in gait restoration in the elderly, a drop foot neuroprosthesis has also been developed by integrating a wearable sensor-based system for gait monitoring with a commercially available PC-controlled stimulator (RehaStim, Hasomed GmbH). Our research aims to evaluate FES as a potential training to improve gait and balance performances especially among less active elderly. We believe that the development of this mobile system for gait monitoring will provide the basis for a long-term evaluation of gait and balance in a large cohort of patients with continuous gait disturbances. Furthermore, it will provide the basis for the control of a robotic walker previously developed at our institution.


The adaptive drop foot stimulator - Multivariable learning control of foot pitch and roll motion in paretic gait.

Abstract: Many stroke patients suffer from the drop foot syndrome, which is characterized by a limited ability to lift (the lateral and/or medial edge of) the foot and leads to a pathological gait. In this contribution, we consider the treatment of this syndrome via functional electrical stimulation (FES) of the peroneal nerve during the swing phase of the paretic foot. A novel three-electrodes setup allows us to manipulate the recruitment of m. tibialis anterior and m. fibularis longus via two independent FES channels without violating the zero-net-current requirement of FES. We characterize the domain of admissible stimulation intensities that results from the nonlinearities in patients' stimulation intensity tolerance. To compensate most of the cross-couplings between the FES intensities and the foot motion, we apply a nonlinear controller output mapping. Gait phase transitions as well as foot pitch and roll angles are assessed in realtime by means of an Inertial Measurement Unit (IMU). A decentralized Iterative Learning Control (ILC) scheme is used to adjust the stimulation to the current needs of the individual patient. We evaluate the effectiveness of this approach in experimental trials with drop foot patients walking on a treadmill and on level ground. Starting from conventional stimulation parameters, the controller automatically determines individual stimulation parameters and thus achieves physiological foot pitch and roll angle trajectories within at most two strides.

Pub.: 12 Jul '16, Pinned: 29 Sep '17

Exploiting kinematic constraints to compensate magnetic disturbances when calculating joint angles of approximate hinge joints from orientation estimates of inertial sensors.

Abstract: Inertial Measurement Units (IMUs) have become a widely used tool for rehabilitation and other application domains in which human motion is analyzed using an ambulatory or wearable setup. Since the magnetic field is inhomogeneous in indoor environments and in the proximity of ferromagnetic material, standard orientation estimation and joint angle calculation algorithms often lead to inaccurate or even completely wrong results. One approach to circumvent this is to exploit the kinematic constraint that is induced by mechanical hinge joints and also by approximate hinge joints such as the knee joint and the finger (interphalangeal) joints of the human body. We propose a quaternion-based method for joint angle measurement for approximate hinge joints moving through inhomogeneous magnetic fields. The method exploits the kinematic constraint to compensate the error that the magnetic disturbances induce in the IMU orientation estimates. This is achieved by realtime estimation and correction of the relative heading (azimuth) error that is caused by the disturbance. Since the kinematic constraint does not allow heading correction when the joint axis is vertical, we extend the proposed method such that it improves accuracy and robustness when the joint is close to that singularity. We evaluate the method by simulations of a quick hand motion and study the effect of inaccurate sensor-to-segment (anatomical) calibration and joint constraint relaxations. As a main result, the proposed method is found to reduce the root-mean-square error of the joint angle from 25.8° to 2.6° in the presence of large magnetic disturbances.

Pub.: 18 Aug '17, Pinned: 29 Sep '17