Wearable computing technology is rapidly proliferating and playing an increasing role in our daily lives. The PI's focus in this research was on developing technology for monitoring human performance within the context of stroke rehabilitation that is applicable to a wide spectrum of applications related to human movement assessment. Robust performance metrics that can be interpreted by a non-expert in sensor technologies enables people to track their well-being in a manner not currently possible. With technology in the home environment, there is the potential to better engage the user in self-monitoring, to increase motivation, and to improve motion strategies for activities related to well-being. From the healthcare professional's perspective, wearable technology in the home could allow the clinician to change the balance of time so as to emphasize educating and working with the patient on enabling tasks, because the wearable technology would provide information on compliance history and progress. The longitudinal data from the sensors would also permit improved evaluation of patient-specific dose-response sensitivity. The human-centered research methodology implemented here also provides new insights into systems modeling heuristics, in particular how to formalize relationships between the human and computer entities for decision making needs.

In this project the capabilities of wearable motion sensing technology were extended through advances in dynamic system modeling and signal processing to account for the underlying variability in motion and compliant structure of the individual with evidence-based approaches to assess the robustness of novel metrics of motion performance. During the performance years, the effort (1) characterized clinical decision making of occupational therapists to support the definition of metrics that would augment clinical assessment capabilities; (2) developed new algorithms for estimating human kinematics (the motions of the body); as well as (3) defining metrics of performance that aligned with operational-relevant clinical decision making for balance, coordination, and compensatory motions. Due to the pandemic, an additional task was included that examined telehealth practices of occupational and physical therapists that complemented the initial characterization of clinical decision making. While the initial motivation for the effort was for clinical applications, these methods were also synergistically applied and implemented to support human performance assessment for other wearable technologies, such as space suits and exoskeletons.

By bridging biomechanics and statistical modelling, new capabilities were developed for wearable motion-sensing devices that integrated relevant nonlinear models with the appropriate uncertainty, which in turn enabled estimation of body motions using a wearable sensor array. By bridging biomechanics with human factors, the decision making models and essential clinical monitoring criteria were uncovered that informed the definition of human movement performance metrics relevant for supporting assessment and plan of care progressions. The human-centered research methodology implemented in this research to define metrics of performance can support decision making across a variety of fields where mobility is important. Methods from this award will continue to be extended to support clinical decision making, but will also be applied to workplace safety, human performance training and readiness, as well as the assessment of how medical interventions, body-worn gear, and wearable technology affect movement.

As part of this effort, the research team engaged with students and teachers in collaboration with the MIT museum and Nord Anglia Education (which has schools around the world and includes a family of more than 50,000 students). Content was developed for teachers and students to support design challenges based on an overall theme of STEAM superheroes. The research team participated in synchronous in-person STEAM teacher workshops and student workshops, as well as developing asynchronous In-School Challenges that could be performed by individual students and student teams with the support of their teachers. The challenges were based on quantifying human performance and informed by ideas related to this research program. The open-ended challenges provided opportunity for students to engage in design thinking in a human-centered community focused mindset that would support their creativity and positive engagement with science and engineering.

Last Modified: 07/08/2021
Modified by: Leia Stirling