Quantifying upper limb movements among wheelchair users using wheelchair propulsion monitoring devices

Wheelchair users face the challenge of using their arms to mobilize their bodies instead of their legs—resulting in pain and injury. Development of tools to measure motions occurring during wheelchair propulsion presents the opportunity to study patterns and activities of wheelchair users to help prevent pain and injury. This study combined measurement tools including accelerometers and a wheel rotation data logger to collect data on activities performed by manual wheelchair users. Twenty-six participants with spinal cord injury completed lab visits of data collection. A model was created from lab data to classify data as propulsion, rest, activities of daily living (ADLs), or being pushed. The best percent accuracies of the classifying model for each activity are as follows: 84.5% for propulsion, 85.6% for rest, 84.6% for ADLs, and 79.9% for being pushed. When applied to data from a user’s natural environment, this model can provide information on average time spent per day in each activity. With future work, the wheelchair propulsion monitoring devices of this study could quantify movement in manual wheelchair users’ natural environments

TOWARDS MONITORING WHEELCHAIR PROPULSION IN NATURAL ENVIRONMENT USING WEARABLE SENSORS

Due to lower limb paralysis, individuals with spinal cord injury (SCI) rely on their upper limbs for activities of daily living (ADLs) and wheelchair propulsion (WP). Previous research has found that specific biomechanical parameters of WP are associated with risk of UE pain and injury. However, the repetitiveness and quality of upper limb movements during WP are unclear. Recently, wearable sensors have been used to collect mobility characteristics of wheelchair users, but little research has looked into using them to monitor the quality of UE movements for WP in the natural environment. The purpose of this thesis was to develop and evaluate a WP monitoring device that can monitor wheelchair users’ activities, and propulsion parameters in the natural environment. This thesis is organized into three studies. The first study aims to develop activity classifiers that can distinguish WP episodes from a range of ADLs. Two classifying models were built using a Machine Learning (ML) technique. The model that yielded the highest accuracy showed an overall accuracy of 88.0%. Time spent on each activity was estimated based on the classifiers, and compared with the video observation. Percentage of difference between the criterion and estimated time ranged from 2.2% to 11.6%. The second study aims to estimate temporal parameters of WP, including the stroke number (SN) and push frequency (PF), using wearable sensors. The estimated SN and PF were compared with the criterion measures using the mean absolute errors (MAE) and mean absolute percentage of error (MAPE). Intraclass Correlation Coefficients were calculated to assess the agreement. The accelerometer placed on the upper arm yielded the highest accuracy with the MAPE of 8.0% for SN and 12.9% for PF. The third study aims to estimate wheelchair propulsion forces. Propulsion forces were estimated from the accelerometer placed on the upper arm using a bagging regression technique. The estimated forces were compared with the criterion. Mean absolute errors (MAE), mean absolute percentage of error (MAPE), were calculated. The results showed an overall MAPE of 17.9%. Intraclass Correlation Coefficients and Bland-Altman plots were used to assess the agreement between the criterion and the estimated force

Street Rehab: Linking Accessibility and Rehabilitation

As part of the Accessible Routes from Crowdsourced Cloud Services project (ARCCS) we conducted a series of experiments using the ARCCS sensor to identify push style of wheelchair users. The aim of ARCCS is to make use of a set of well-calibrated sensors to establish a processing chain that then provides ground truth of known accuracy about location, the nature of the environment, and physiological effort. In this paper we focus on two classification problems 1) The push style employed by people as they push themselves and 2) Whether the person is being pushed by an attendant or pushing themselves (independent of push style). Solving the first enables us to develop a level of granularity to pushing classification which transcends rehabilitation and accessibility. The first problem was solved using a wrist-mounted ARCCS sensor, and the second using a wheel-mounted ARCCS sensor. Push styles were classified between semi-circular and arc styles in both indoor and outdoor environments with a high-decrees of precision and recall (>95%). The ARCCS sensor also proved capable of discerning attendant from self-propulsion with near perfect accuracy and recall, without the need for a body-worn sensor

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

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

Development of an artificial intelligence algorithm for the analysis of wheelchair movements

Monitoring wheelchair user movement is an essential task for assessing a wheelchair user’s mobility and helping them maintain an active lifestyle. Research has shown that increased mobility leads to healthier overall lifestyles, and that people with disabilities are at an increased risk for sedentary lifestyles and the health problems associated with that lifestyle, including cardiovascular disease, obesity, and the development of pressure ulcers (WHO, 2014). Existing technology for analyzing wheelchair user mobility data requires the use of external sensors that must be purchased and maintained (Warms & Belza, 2004). To improve the ease by which mobility data is maintained and analyzed, a wheelchair user can utilize existing technology, such as smart mobile devices, to gather and analyze motion data. This study will focus on the development of a recurrent neural network (RNN) that is trained using wheelchair user data collected from smart devices attached to the wheelchair or wheelchair user. The benefit of collecting data this way is that it does not require the use of additional sensors or equipment, as most wheelchair users will already have access to a smart device capable of collecting movement data. The study found that it was feasible to meaningfully analyze data gathered from a smart device using an RNN. The raw data is analyzed with the RNN to gather information about the mobility of a wheelchair user. The final analysis includes the total time spent moving, number of bouts of movement, and the longest bout of movement. This resulting data could be used by a wheelchair user or healthcare professional to help assess healthy lifestyle habits

INVESTIGATION OF TERRAIN EFFECTS ON WHEELCHAIR PROPULSION AND VALIDITY OF A WHEELCHAIR PROPULSION MONITOR

This thesis is composed of two studies related to wheelchair propulsion biomechanics. The first study investigated the impact of cross-slope and surface roughness on wheelchair propulsion. Fifteen manual wheelchair users propelled across a five-meter platform which were set to level, 1°, or 2° cross slope, and attached with one of three surfaces including Teflon (slippery), wood (normal), and blind guide (rough). The study found main effects of both cross slope and surface roughness on stroke number and sum of work, and a main effect of cross slope on velocity. Subjects travelled slower, used more strokes, and expended more work with increasing cross slope. Subjects also used more strokes when propelling on the slippery and rough surfaces than on the level surface. They expended more work when propelling on the rough surface than on the level surface. When looking into bilateral propulsion parameters, we found that peak resultant force, peak wheel torque, and sum of work became significantly asymmetrical with the increase of cross slopes. Exposure to biomechanics loading can be reduced by avoiding slippery, rough, and cross slopes when possible. The second study consisted of a preliminary analysis on the validity of a wheelchair propulsion monitor (WPM) in estimating wheelchair propulsion biomechanics. The WPM integrates three devices including a wheel rotation datalogger, and an accelerometry-based device on the upper arm and underneath the wheelchair seat, respectively. Five wheelchair users were asked to push their own wheelchairs fitted with a SMARTWheel over level and sloped surfaces on two separate visits. The estimated stroke number and cadence by the WPM were consistent with the criterion measures by the SMARTWheel (ICC= 0.99 for stroke number, ICC=0.97 for cadence) with less than 5% absolute percentage errors for stroke number and 9% for cadence. The peak resultant force and wheel torque could be predicted to some extent by acceleration features on an individual subject basis. The study demonstrated the potential of the WPM in tracking wheelchair propulsion characteristics in the natural environment of wheelchair users

Use of a Low Cost, Chest-Mounted Accelerometer to Evaluate Transfer Skills of Wheelchair Users During Everyday Activities

BACKGROUND: Transfers are an important skill for many wheelchair users. However, they have also been related to the risk of falling or developing upper limb injuries. Transfer abilities are usually evaluated in clinical settings or biomechanics laboratories and these methods of assessment are poorly suited to evaluation in real and unconstrained world settings where transfers take place. OBJECTIVE: The objective of this paper is to develop a strategy to enable transfer quality evaluation and improve the predictive accuracy of transfer detection using a single wearable low cost accelerometer. METHODS: We collected data from nine wheelchair users wearing tri-axial accelerometer on their chest while performing transfers to and from car seats and home furniture. We then extracted significant features from accelerometer data based on biomechanical considerations and previous relevant literature and used machine learning algorithms to evaluate the performance of wheelchair transfers and detect their occurrence from a continuous time series of data. RESULTS: Results show that the best predictive accuracy for Automatic Transfer Quality Evaluation was obtained with Support Vector Machine (SVM) classifiers when determining use of head-hip relationship (75.93%) and smoothness of landing (79.62%), when the start and end of the transfer are known. Automatic Transfer Detection reaches an accuracy of 87.8% using Multinomial Logistic Regression (MLR) classifiers, which is in line with the state of the art in this context. However, we achieve these results using only a single sensor and collecting data in a more ecological manner. CONCLUSIONS: The use of a single chest-placed accelerometer shows a predictive accuracy of over 75% for algorithms applied independently to both transfer evaluation and monitoring. This points to the opportunity for designing ubiquitous technology for personalized skill development interventions targeting wheelchair users. However, monitoring transfers still requires the use of external inputs or extra sensors to identify start and end of the transfer, which are needed to perform an accurate evaluation

EVALUATION OF ACCELEROMETER-BASED ACTIVITY MONITORS TO ASSESS ENERGY EXPENDITURE OF MANUAL WHEELCHAIR USERS WITH SPINAL CORD INJURY

A primary objective of the study was to determine the validity of a SenseWear (SW) activity monitor (AM) in assessing Energy Expenditure (EE) of manual wheelchair users with spinal cord Injury (SCI) while resting and performing three types of physical activities including wheelchair propulsion, arm-ergometer exercise, and deskwork. A secondary objective of the study was to build and validate a new EE prediction model for a SW AM for the physical activities performed in the study. A tertiary objective was to examine the relationship between the criterion EE and three activity monitors including the ActiGraph, the RT3 on arm, and RT3 on waist. Ten manual wheelchair users with SCI were recruited to participate in this pilot study.The results indicate that EE estimated by SenseWear AM with the default EE equationfor resting was close (0.2%) to the criterion EE in manual wheelchair users with SCI. However, the SW AM overestimated EE during deskwork, wheelchair propulsion and arm-ergometry exercise by 6.5%, 105% and 32%, respectively.From the investigation, we found that the EE estimated by SW AM using the new regression equation model significantly improved its performance in manual wheelchair users with SCI. The Intraclass Correlation Coefficient of EE estimated by SW using new prediction equation and the criterion EE were excellent (0.90) and moderate (0.74) with percent errors reduced to 17.4% and 7.0% for wheelchair propulsion and arm-ergometry exercise, respectively. The new prediction equation for SW AM was able to differentiate and discriminate (sensitive)EE estimation in physical activities like wheelchair propulsion and arm-ergometer exercises in manual wheelchair users with SCI indicating that it has a potential to be used in manual wheelchair users with SCI.In addition, the variance explained by RT3 (R2 = 0.68, p<0.001) on arm and the ActiGraph (R2 = 0.59, p<0.001) on the wrist wrist indicate that AMs placed on an arm or wrist may be able to better predict EE compared to the AM on the waist