Mathematical Models Of Human Movement For Assistive Models

Children born with non-degenerative motor diseases often grow to experience increasing mobility restrictions as they age that are not due to the progression of their illness. One of the reasons this occurs is because of sub-optimal movement patterns that these children develop to overcome their mobility limitations. Often the motion they can achieve under the constraints of their illness is too tiring to be sustainable as they grow and their body weight increases. To overcome these difficulties and to try to provide life-long mobility solutions, children and patients with motor disabilities follow rigorous physiotherapy programs and in some circumstances it is possible to perform surgery to attempt to correct movement patterns that are unsustainable for the patient. Whatever the situation, the patients are followed very closely by their doctors and therapists to understand their individual conditions.

There are two areas where we can benefit from recent technological advances. Firstly, though a patient's condition might be understood in detail by their close family and patients, doctors and therapists, it is difficult to compare the condition and treatment of different patients, because there is no quantitative valuation of mobility. At present, mobility is classified using a number of scales for different types of motion (for example, the Gross Motor Function Classification System (GMFCS), the Manual Ability Classification System (MACS), the Communication Function Classification System (CFCS) and the Eating and Drinking Ability Classification System (EDACS), the four functional classification systems most often used with Cerebral Palsy), but these all divide mobility into very large categories, and do not capture the differences between patients particularly well. Secondly, and partly as a consequence of the first challenge, it is difficult to predict whether treatments will have the desired effect, particularly in the case of more invasive surgical procedures. In addition, a recent study suggests that training with a robotic exoskeleton can have similar effects to hamstring lengthening surgery in children with Cerebral Palsy.

The aim of this research is to develop quantitative methods of assessing mobility and the benefit of treatment using Inertial Measurement Units (IMUs). This would first allow for the comparison of motor disabilities across a much larger pool of patients, and second for more rigorous understanding of the development of mobility with time. As an additional benefit, monitoring using IMUs allows for the possibility of remote, home-environment assessment of patients, giving a more comprehensive understanding of the difficulties the patients face at home, or the changes in mobility they experience when they are in unfamiliar environments.

The system will consist of six IMUs, placed at the wrists, ankles and on the torso. The data from these sensors, combined with a mechanical model of the human body, including joint constraints, will be used to recover the full sequence of motion of the patient. The low number of sensors allows the motion to be unconstrained, and would allow for the sensors to be integrated easily into orthoses and splints. In comparison to camera-based motion capture systems, IMUs allow measurement to take place 'in the wild', rather than within a restricted lab environment. Since the IMU data are numerical, and also describe additional parameters, such as the smoothness or range of the motion, they can be used to more easily draw comparisons across different patients, and better understand how the same patient's mobility is progressing over larger time scales. In particular, it would be interesting to perform a quantitative comparison of the effect of surgical intervention with assisted physiotherapy using an exoskeleton in patients with Cerebral Palsy.

EPSRC research areas: Assistive technology, rehabilitation and musculoskeletal biomechanics