Towards a Wearable Wheelchair Monitor: Classification of push style based on inertial sensors at multiple upper limb locations

Measuring manual wheelchair activity by using wearable sensors is becoming increasingly common for rehabilitation and monitoring purposes. Until recently most research has focused on the identification of activities of daily living or on counting the number of strokes. However, how a person pushes their wheelchair - their stroke pattern - is an important descriptor of the wheelchair user's quality of movement. This paper evaluates the capability of inertial sensors located at different upper limb locations plus the wheel of the wheelchair, to classify two types of stroke pattern for manual wheelchairs: semicircle and arc. Data was collected using bespoke inertial sensors with a wheelchair fixed to a treadmill. Classification was completed with a linear SVM algorithm, and classification performance was computed for each sensor location in the upper limb, and then in combination with wheel sensor. For single sensors, forearm location had the highest accuracy (96%) followed by hand (93%) and arm (90%). For combined sensor location with wheel, best accuracy came in combination with forearm. These results set the direction towards a wearable wheelchair monitor that can measure the quality as well as the quantity of movement and which offers multiple on-body locations for increased usability

MODEL, FRAMEWORK, AND PLATFORM OF HEALTH PERSUASIVE SOCIAL NETWORK

Persuasive technology (PT) has the potential to support individuals to perform self-management and social support as a part of health behavior change. This has led a few researchers in the intersection of the areas of health behavior change and software engineering to apply behavior change and persuasion theories to software development practices, enabling them to create innovative design principles and development-evaluation frameworks. Unfortunately these are too general for designing and evaluating health PT. Therefore, this dissertation proposes a model, framework, and platform of PT specifically designed for health intervention. The model and framework inform what, why, and how conceptually the suggested and required health behavior change strategies should be transformed into system features; and the platform explains how technically the transformation should be done. The platform includes functional requirements and provides most of the basic and standard computer code to develop the system features of such PT. The model, framework, and platform were designed to work with various health behavior change programs. Nevertheless, in this dissertation, they support health behavior change for physical activity. As an implementation of and tool to evaluate the model, framework, and platform, a technology called Persuasive Social Network for Physical Activity (PersonA) is introduced. PersonA is a combination of automatic input of physical activity data, a smart phone, and social networking. Two systems (SocioPedometer and PAMS) as leverages of PersonA have been developed and evaluated. The model, framework, and platform were evaluated based on the results of SocioPedometer’s usability testing and 4-week trials (n=14) and on PAMS’s usability testing (n=5). The results suggest that the systems were usable and accessible and that users were satisfied and enjoyed using it. Additional evaluations to the model and framework were conducted with the main purpose of eliciting users’ preferences with respect to the characteristics and system features proposed in the model and framework. They rated most of the characteristics as extremely important (average 4.27 of a 5.00 maximum) and most of the system features as very important (average of 4.09). The platform allowed the two systems to be easily developed by customizing the data input and information presented

Weight monitoring in bed using E-scale

Wheelchair users are more than twice as likely to be obese compared to the general population. This is not surprising considering that it is challenging for wheelchair users to monitor their weight as well as maintain an active lifestyle, both of which are factors that strongly influence a person’s ability to manage their weight. The E-scale is a weight monitoring system that was designed for wheelchair users to be able to weigh themselves frequently in their homes. It is comprised of a set of weight sensors that are placed under the legs of a bed or other piece of furniture and passively and continuously measures the weight on each bed leg. This dissertation focuses on the design evolution of the E-scale, and specifically on developing and testing two key aspects of the E-scale that are related to its commercial viability. To make sure that the E-scale can be used in the common scenario where beds are shared by a couple, algorithms for monitoring and differentiating the weight of multiple people using the same bed were developed. To test the usefulness of the E-scale, a weight loss study for wheelchair users was conducted to determine if the E-scale is a feasible technology to use along with a standard behavioral weight loss program adapted specifically for wheelchair users. The results of these two studies as well as a description of future work and preliminary discussions of other applications of the E-scale are reported

Physical Activity Monitoring System for Manual Wheelchair Users

People with disabilities who rely on manual wheelchairs as their primary means of mobility face daily challenges such as mobility limitations and environmental barriers when engaging in regular physical activity. Therefore, our research addressed the need for a valid and reliable physical activity monitor to assess and quantify physical activities among manual wheelchair users (MWUs) in free-living environments. Providing an accurate estimate of physical activity (PA) levels in MWUs can assist researchers and clinicians to quantify day-to-day PA levels, leading to recommendations for a healthier lifestyle. In the first stage we developed and evaluated new classification and EE estimation models for MWUs with spinal cord injury (N=45) using SenseWear, an off-the-shelf activity monitor, designed for the general population without disabilities. The results suggested that SenseWear can be used by researchers and clinicians to detect and estimate the EE for four activities tested in our study. The second phase of our research project developed an activity monitor especially designed for MWUs. Previous research in community participation of MWUs and the studies discussed above found that wheelchair mobility characteristics are necessary to study PA patterns in MWUs. This requirement led us to develop and evaluate a Physical Activity Monitor System (PAMS) composed of two components: a gyroscope based wheel rotation monitor (G-WRM for tracking wheelchair mobility and an accelerometer that quantifies upper arm movement. We tested PAMS in 45 MWUs with SCI in the structured (laboratory) and semi-structured environments (National Veterans Wheelchair Gamers 2012). In addition, we also tested a subsection of this population (N=20) a second time, in their home environments. The PAs were classified as resting, armergometry, other sedentary activities, activities involving some wheelchair movement, propulsion, basketball and caretaker pushing. The EE estimation results (error: -9.8%) and the classification results (accuracy: 89.3%) indicate that PAMS can reliably track wheelchair-based activities in laboratory and home environments. Furthermore, we used participatory action design to evaluate the usability of PAMS in six MWUs with SCI. The usability study indicated that users were very satisfied with PAMS and the information provided by the smartphone to the users about their PA levels