Maria Jones

Effect of Wheelchair Tilt-in-Space and Recline Angles on Skin Perfusion Over the Ischial Tuberosity in People With Spinal Cord Injury

OBJECTIVE To investigate the efficacy of wheelchair tilt-in-space and recline on enhancing skin perfusion over the ischial tuberosity in wheelchair users with spinal cord injury (SCI). DESIGN Repeated-measures, intervention, and outcomes-measure design. SETTING A university research laboratory. PARTICIPANTS Wheelchair users with SCI (N=11; 9 men, 2 women; mean ± SD age, 37.7±14.2y; body mass index, 24.7±2.6kg/m(2); duration of injury, 8.1±7.5y). INTERVENTIONS Protocols (N=6) of various wheelchair tilt-in-space and recline angles were randomly assigned to participants. Each protocol consisted of a 5-minute sitting-induced ischemic period and a 5-minute wheelchair tilt-in-space and recline pressure-relieving period. Participants sat in a position without tilt or recline for 5 minutes and then sat in 1 of 6 wheelchair tilted and reclined positions, including (1) 15° tilt-in-space and 100° recline, (2) 25° tilt-in-space and 100° recline, (3) 35° tilt-in-space and 100° recline, (4) 15° tilt-in-space and 120° recline, (5) 25° tilt-in-space and 120° recline, and (6) 35° tilt-in-space and 120° recline. A 5-minute washout period (at 35° tilt-in-space and 120° recline) was allowed between protocols. MAIN OUTCOME MEASURES Laser Doppler flowmetry was used to measure skin perfusion over the ischial tuberosity in response to changes in body positions caused by performing wheelchair tilt-in-space and recline. Skin perfusion response to wheelchair tilt-in-space and recline was normalized to skin perfusion of the upright seated position (no tilt/recline). RESULTS Combined with 100° recline, wheelchair tilt-in-space at 35° resulted in a significant increase in skin perfusion compared with the upright seated position (no tilt/recline; P<.05), whereas there was no significant increase in skin perfusion at 15° and 25° tilt-in-space (not significant). Combined with 120° recline, wheelchair tilt-in-space at 15°, 25°, and 35° showed a significant increase in skin perfusion compared with the upright seated position (P<.05). CONCLUSIONS Our results indicate that wheelchair tilt-in-space should be at least 35° for enhancing skin perfusion over the ischial tuberosity when combined with recline at 100° and should be at least 25° when combined with recline at 120°. Although smaller angles of wheelchair tilt-in-space and recline are preferred by wheelchair users for functional purposes, wheelchair tilt-in-space less than 25° and recline less than 100° may not be sufficient for effective pressure reduction for enhancing skin perfusion over the ischial tuberosity in people with SCI.

Effect of durations of wheelchair tilt-in-space and recline on skin perfusion over the ischial tuberosity in people with spinal cord injury.

OBJECTIVE To compare the efficacy of various durations of wheelchair tilt-in-space and recline on enhancing skin perfusion over the ischial tuberosity in people with spinal cord injury (SCI). DESIGN Repeated-measures, intervention and outcomes measure design. SETTING University research laboratory. PARTICIPANTS Power wheelchair users with SCI (N=9). INTERVENTIONS Three protocols of various durations (3min, 1min, and 0min) of wheelchair tilt-in-space and recline were randomly assigned to the participants. Each protocol consisted of a baseline 15-minute sitting, a duration of 0- to 3-minute reclined and tilted, a second 15-minute sitting, and a 5-minute recovery. The position at the baseline and the second sitting was no tilt/recline of the participant and at the reclined and tilted and recovery was at 35° tilt-in-space and 120° recline. MAIN OUTCOME MEASURES Skin perfusion response to tilt and recline was assessed by laser Doppler and was normalized to mean skin perfusion at the baseline sitting. RESULTS The results showed that mean skin perfusion during recovery at the 3-minute duration was significantly higher than that at the 1-minute duration (P<.017) and mean skin perfusion was not significantly different between the 1-minute and 0-minute durations (not significant). Skin perfusion during the second sitting was significantly higher at the 3-minute duration than at the 1-minute and 0-minute durations (P<.017). CONCLUSIONS Our findings suggest that performing the 3-minute duration of wheelchair tilt-in-space and recline is more effective than the 1-minute duration in enhancing skin perfusion of weight-bearing soft tissues.

Capturing and analyzing wheelchair maneuvering patterns with mobile cloud computing

Power wheelchairs have been widely used to provide independent mobility to people with disabilities. Despite great advancements in power wheelchair technology, research shows that wheelchair related accidents occur frequently. To ensure safe maneuverability, capturing wheelchair maneuvering patterns is fundamental to enable other research, such as safe robotic assistance for wheelchair users. In this study, we propose to record, store, and analyze wheelchair maneuvering data by means of mobile cloud computing. Specifically, the accelerometer and gyroscope sensors in smart phones are used to record wheelchair maneuvering data in real-time. Then, the recorded data are periodically transmitted to the cloud for storage and analysis. The analyzed results are then made available to various types of users, such as mobile phone users, traditional desktop users, etc. The combination of mobile computing and cloud computing leverages the advantages of both techniques and extends the smart phones capabilities of computing and data storage via the Internet. We performed a case study to implement the mobile cloud computing framework using Android smart phones and Google App Engine, a popular cloud computing platform. Experimental results demonstrated the feasibility of the proposed mobile cloud computing framework.

Development of intelligent model to determine favorable wheelchair tilt and recline angles for people with spinal cord injury

Machine-learning techniques have found widespread applications in bioinformatics. Such techniques provide invaluable insight on understanding the complex biomedical mechanisms and predicting the optimal individualized intervention for patients. In our case, we are particularly interested in developing an individualized clinical guideline on wheelchair tilt and recline usage for people with spinal cord injury (SCI). The current clinical practice suggests uniform settings to all patients. However, our previous study revealed that the response of skin blood flow to wheelchair tilt and recline settings varied largely among patients. Our finding suggests that an individualized setting is needed for people with SCI to maximally utilize the residual neurological function to reduce pressure ulcer risk. In order to achieve this goal, we intend to develop an intelligent model to determine the favorable wheelchair usage to reduce pressure ulcers risk for wheelchair users with SCI. In this study, we use artificial neural networks (ANNs) to construct an intelligent model that can predict whether a given tilt and recline setting will be favorable to people with SCI based on neurological functions and SCI injury history. Our results indicate that the intelligent model significantly outperforms the traditional statistical approach in accurately classifying favorable wheelchair tilt and recline settings. To the best of our knowledge, this is the first study using intelligent models to predict the favorable wheelchair tilt and recline angles. Our methods demonstrate the feasibility of using ANN to develop individualized wheelchair tilt and recline guidance for people with SCI.

Using Artificial Neural Network to Determine Favorable Wheelchair Tilt and Recline Usage in People with Spinal Cord Injury: Training ANN with Genetic Algorithm to Improve Generalization

People with spinal cord injury (SCI) are at risk for pressure ulcers because of their poor motor function and consequent prolonged sitting in wheelchairs. The current clinical practice typically uses the wheelchair tilt and recline to attain specific seating angles (sitting postures) to reduce seating pressure in order to prevent pressure ulcers. The rationale is to allow the development of reactive hyperemia to re-perfuse the ischemic tissues. However, our study reveals that a particular tilt and recline setting may result in a significant increase of skin perfusion for one person with SCI, but may cause neutral or even negative effect on another person. Therefore, an individualized guidance on wheelchair tilt and recline usage is desirable in people with various levels of SCI. In this study, we intend to demonstrate the feasibility of using machine-learning techniques to classify and predict favorable wheelchair tilt and recline settings for individual wheelchair users with SCI. Specifically, we use artificial neural networks (ANNs) to classify whether a given tilt and recline setting would cause a positive, neutral, or negative skin perfusion response. The challenge, however, is that ANN is prone to over fitting, a situation in which ANN can perfectly classify the existing data while cannot correctly classify new (unseen) data. We investigate using the genetic algorithm (GA) to train ANN to reduce the chance of converging on local optima and improve the generalization capability of classifying unseen data. Our experimental results indicate that the GA-based ANN significantly improves the generalization ability and outperforms the traditional statistical approach and other commonly used classification techniques, such as BP-based ANN and support vector machine (SVM). To the best of our knowledge, there are no such intelligent systems available now. Our research fills in the gap in existing evidence.

A novel mobile-cloud system for capturing and analyzing wheelchair maneuvering data: A pilot study.

ABSTRACT The purpose of this pilot study was to provide a new approach for capturing and analyzing wheelchair maneuvering data, which are critical for evaluating wheelchair users’ activity levels. We proposed a mobile-cloud (MC) system, which incorporated the emerging mobile and cloud computing technologies. The MC system employed smartphone sensors to collect wheelchair maneuvering data and transmit them to the cloud for storage and analysis. A k-nearest neighbor (KNN) machine-learning algorithm was developed to mitigate the impact of sensor noise and recognize wheelchair maneuvering patterns. We conducted 30 trials in an indoor setting, where each trial contained 10 bouts (i.e., periods of continuous wheelchair movement). We also verified our approach in a different building. Different from existing approaches that require sensors to be attached to wheelchairs’ wheels, we placed the smartphone into a smartphone holder attached to the wheelchair. Experimental results illustrate that our approach correctly identified all 300 bouts. Compared to existing approaches, our approach was easier to use while achieving similar accuracy in analyzing the accumulated movement time and maximum period of continuous movement (p > 0.8). Overall, the MC system provided a feasible way to ease the data collection process and generated accurate analysis results for evaluating activity levels.

Retrospective Analysis of Predictors of Proficient Power Mobility in Young Children With Severe Motor Impairments

OBJECTIVES To determine if child characteristics, maternal education, intervention parameters, type of wheelchair control mechanism, or a combination of these variables were associated with proficient power mobility skills in children with severe motor impairments aged 14 to 30 months; and to determine if performance on the Wheelchair Skills Checklist (WSC) was associated with performance on the Powered Mobility Program (PMP). DESIGN Secondary data analyses on data collected from 2 previously completed randomized controlled trials (RCTs). SETTING Intervention and outcomes measurements took place in natural environments. PARTICIPANTS Participants included children who were assigned to the intervention groups in 2 RCTs (N=31). INTERVENTION Children practiced maneuvering individually customized power wheelchairs for 12 months in natural environments. MAIN OUTCOME MEASURES Proficiency was assessed using the WSC and the PMP. The Battelle Developmental Inventory and Merrill-Palmer-Revised were used to assess baseline cognition and motor skills. Baseline mobility was assessed using the Pediatric Evaluation of Disability Inventory. RESULTS Cognition, fine motor skills, and wheelchair control mechanism were associated with proficiency. Cognition, type of wheelchair control, and diagnosis all predicted proficiency while controlling for other covariates using multiple regression analysis. Agreement between the WSC and PMP was 94.7%. CONCLUSIONS Cognition, type of wheelchair control, and diagnosis might predict power mobility proficiency in young children with severe motor impairments. These factors however should not be used to determine whether a child has the opportunity to participate in a training program. Agreement between the WSC and PMP could help researchers and clinicians compare results across studies that use only one of these outcome measures.

Knowledge Translation of the Gross Motor Function Classification System Among Pediatric Physical Therapists.

Purpose: To learn where pediatric physical therapists in the United States are in the process of knowledge translation of the Gross Motor Function Classification System (GMFCS). Methods: Links to an online survey were distributed electronically. Results: All 283 respondents reported hearing about the GMFCS, 95% agreed it was useful, 81% reported they were confident in their ability to use it, 77% reported they use it, and 42% reported they use it consistently. Therapists primarily used the GMFCS to predict gross motor function, set realistic goals, and anticipate need for assistive technology. The American Physical Therapy Association Section on Pediatrics members were more likely than nonmembers to agree the GMFCS is useful, they are able to use it, that they use it, and that they use it consistently. Conclusions: The majority of therapists responding use the GMFCS, but not consistently. Video Abstract: For more insights from the authors, see Supplemental Digital Content 1, available at http://links.lww.com/PPT/A91.

Development of Intelligent Model for Personalized Guidance on Wheelchair Tilt and Recline Usage for People with Spinal Cord Injury: Methodology and Preliminary Report

Wheelchair tilt and recline functions are two of the most desirable features for relieving seating pressure to decrease the risk of pressure ulcers. The effective guidance on wheelchair tilt and recline usage is therefore critical to pressure ulcer prevention. The aim of this study was to demonstrate the feasibility of using machine learning techniques to construct an intelligent model to provide personalized guidance to individuals with spinal cord injury (SCI). The motivation stems from the clinical evidence that the requirements of individuals vary greatly and that no universal guidance on tilt and recline usage could possibly satisfy all individuals with SCI. We explored all aspects involved in constructing the intelligent model and proposed approaches tailored to suit the characteristics of this preliminary study, such as the way of modeling research participants, using machine learning techniques to construct the intelligent model, and evaluating the performance of the intelligent model. We further improved the intelligent models prediction accuracy by developing a two-phase feature selection algorithm to identify important attributes. Experimental results demonstrated that our approaches held the promise: they could effectively construct the intelligent model, evaluate its performance, and refine the participant model so that the intelligent models prediction accuracy was significantly improved.

Towards an intelligent system for clinical guidance on wheelchair tilt and recline usage

We propose to construct an intelligent system for clinical guidance on how to effectively use power wheelchair tilt and recline functions. The motivations fall into the following two aspects. (1) People with spinal cord injury (SCI) are vulnerable to pressure ulcers. SCI can lead to structural and functional changes below the injury level that may predispose individuals to tissue breakdown. As a result, pressure ulcers can significantly affect the quality of life, including pain, infection, altered body image, and even mortality. (2) Clinically, wheelchair power seat function, i.e., tilt and recline, is recommended for relieving sitting-induced pressures. The goal is to increase skin blood flow for the ischemic soft tissues to avoid irreversible damage. Due to variations in the level and completeness of SCI, the effectiveness of using wheelchair tilt and recline to reduce pressure ulcer risks has considerable room for improvement. Our previous study indicated that the blood flow of people with SCI may respond very differently to wheelchair tilt and recline settings. In this study, we propose to use the artificial neural network (ANN) to predict how wheelchair power seat functions affect blood flow response to seating pressure. This is regression learning because the predicted outputs are numerical values. Besides the challenging nature of regression learning, ANN may suffer from the overfitting problem which, when occurring, leads to poor predictive quality (i.e., cannot generalize). We propose using the particle swarm optimization (PSO) algorithm to train ANN to mitigate the impact of overfitting so that ANN can make correct predictions on both existing and new data. Experimental results show that the proposed approach is promising to improve ANNs predictive quality for new data.