Wheelchair Safety Research Articles

Wheelchair safety system using fuzzy logic controller to avoid obstruction

Wheelchair safety system using fuzzy logic controller to avoid obstruction

User Perspectives and Psychophysiological Manifestations of Fatigue with Trunk Orthosis for Dystrophinopathy Patients.

The chair-mounted passive trunk orthosis (CMPTO) is designed to enhance wheelchair safety for individuals with dystrophinopathy during their daily activities. Given the disease's progressive nature, it is crucial to ensure that assistive devices are carefully evaluated to prevent overexertion. This study aims to assess the CMPTO's user experience and its impact on fatigue-related psychophysiological measurements. We conducted electromyography (EMG) evaluations of four trunk muscles and assessed perceived exertion using the Borg CR-10 scale in 40 healthy subjects while they performed seated maximal reaching tasks with the CMPTO. Additionally, fifteen dystrophinopathy patients evaluated the CMPTO for usability with the System Usability Scale. Paired t-tests were employed to compare the median frequency (MDF) of EMG signals, the Wilcoxon signed-rank test for evaluating exertion, and the Mann-Whitney U test to compare the usability reported by patients to those of healthy subjects. The 4-way ANOVA revealed that MDF patterns were significantly influenced by task orientation for each muscle. The CMPTO did not cause a significant reduction in the MDF. Tasks requiring greater trunk rotation were perceived as more exhaustive. Patients reported acceptable usability with the CMPTO, with scores higher than those of healthy subjects. The CMPTO's usability was comprehensively evaluated in both healthy subjects and patients with dystrophinopathy. Our findings indicate that the CMPTO can be safely used by individuals with dystrophinopathy as an assistive device to improve seated comfort and functional abilities.

Carbon monoxide poisoning manifesting with rhabdomyolysis, Takotsubo syndrome, and skin lesion: A case report

Carbon monoxide (CO) poisoning is characterized by non-specific and protean clinical manifestations, exhibiting a highly variable presentation. We present a case of an 83-year-old female Alzheimer’s patient, a non-smoker, who was found unconscious in her wheelchair by her caregiver at home. The heating in the apartment was powered by liquefied petroleum gas. On admission to the emergency department, her vital signs were normal, but she was unconscious and slightly dyspneic. The examination revealed red-colored skin lesions in the submammary area, where the wheelchair safety belt was firmly fastened. The rest of the physical examination results were unremarkable. The carboxyhemoglobin level was 23%, and there was an increase in creatine phosphokinase and troponin I levels. On the 2nd day of hospitalization in the emergency ward, the electrocardiogram revealed negative T waves in the V1–V6 leads, while the bedside echocardiogram revealed apical ballooning of the left ventricle. The myocardial perfusion examination yielded a negative result for myocardial ischemia. In the management of patients with CO poisoning, it is important for emergency physicians to assess the presence of multiple organ and tissue disorders. Consequently, the patient was diagnosed with CO poisoning with skin, skeletal, and cardiac muscle involvement.

Promoting sustainable safety: Integrating fall detection for person and wheelchair safety

Fall detection systems are crucial for ensuring the safety of the elderly, especially those who are wheelchair-bound. A potential remedy involves promptly detecting human falls in near real-time to facilitate rapid assistance. While various methods have been suggested for fall detectors, there remains a necessity to create precise and sturdy architectures, methodologies, and protocols for detecting falls, particularly among elderly individuals, especially those using wheelchairs. The objective is to design an affordable and dependable IoT-based system for detecting falls in wheelchair users, alerting nearby individuals for assistance and promote sustainable safety. The setup includes a MEMS Sensor, GSM module, and Arduino UNO microcontroller for detecting falls, with the goal of securing the well-being and promoting independent living for the elderly.

Case Report

Abstract Falls from wheelchairs are common, particularly in persons with a spinal cord injury. Patients who experience falls frequently incur physical and psychological damages. The underlying causes of falls vary, although one of the most important themes is associated with poor balance. Factors that contribute to ineffective balance include poor trunk and abdominal muscle strength, inability to maintain static or dynamic trunk control, diminished sensation, improper equipment usage, muscle spasms, unsafe speed of mobilization, and lack of protective straps. The literature regarding fall prevention of pediatric patients with a spinal cord injury is very limited. Guidance is needed for this vulnerable population to prevent falls from wheelchairs. Instructions for wheelchair safety must be incorporated into patient and family caregiver education. Behavioral change theory provides a framework for this work. This article is intended to close the gap by discussing the incidence of falls, suggesting the provision of teaching self-management, and introducing suggestions for developing an interdisciplinary fall prevention program.

Risk Factor Attitude Survey and Step of Road Detection Method About Wheelchair of Elderly Person

We propose a step-detection system to improve the safety of electric wheelchairs for the elderly. In automobile driving support systems, roads and other environments are being improved, and collision detection and avoidance for vehicles and other pedestrians are being studied and prioritized. However, in the driving environment of electric wheelchairs, it is important to deal with bumps and one-way slopes on the road. In this study, we conducted a survey on elderly people’s awareness of risk factors related to wheelchairs and categorized these risk factors. In addition, we confirmed the usefulness of the step-detecting device by conducting a driving experiment.

Wheelchair Safety Device for Children and Handicapped

Abstract: Falling down from a wheelchair is among the significant reasons for clinical issues that are faced by older individuals, small babies, and handicapped individuals. These individuals will more often harm themselves from tumbling down when they are alone. When a falling occasion happens, clinical consideration needs to give promptly to diminish the gamble of the faller from getting serious wounds that might prompt demise. A few advances have been created and a few used webcams to screen their exercises. Nevertheless, the cost of movement and foundation is exorbitant and only relevant for the indoor environment. Some clients additionally stressed over their security issues. The current marketed gadget is by wearing a wearable remote crisis transmitter which confines the development of the client and produces a high Phoney problem. This examination proposed a wheelchair-individual fall identification framework using Arduino and gyroscopic sensor which is financially savvy and dependable to identify falls and alarm encompassing to call for help. For fall discovery, the mpu6050 sensor GSM module and Arduino are carried out into the framework. At the point when fall occasions happen, the information incorporating the area of the wheelchair will be sent to the caretakers of that person and the alarm fixed to the device will be turned on automatically. Also, this framework requires less execution cost and gives a speedy reaction. It can introduce in the current business wheelchair. For the future proposal, a superior GUI configuration can be executed into the framework other than that, more discovery frameworks can be added to build the precision of the framework. Keywords: Sensor-Arduino, Wheelchair Fall detection, GSM module, mpu6050

Classification of the Sidewalk Condition Using Self-Supervised Transfer Learning for Wheelchair Safety Driving.

The demand for wheelchairs has increased recently as the population of the elderly and patients with disorders increases. However, society still pays less attention to infrastructure that can threaten the wheelchair user, such as sidewalks with cracks/potholes. Although various studies have been proposed to recognize such challenges, they mainly depend on RGB images or IMU sensors, which are sensitive to outdoor conditions such as low illumination, bad weather, and unavoidable vibrations, resulting in unsatisfactory and unstable performance. In this paper, we introduce a novel system based on various convolutional neural networks (CNNs) to automatically classify the condition of sidewalks using images captured with depth and infrared modalities. Moreover, we compare the performance of training CNNs from scratch and the transfer learning approach, where the weights learned from the natural image domain (e.g., ImageNet) are fine-tuned to the depth and infrared image domain. In particular, we propose applying the ResNet-152 model pre-trained with self-supervised learning during transfer learning to leverage better image representations. Performance evaluation on the classification of the sidewalk condition was conducted with 100% and 10% of training data. The experimental results validate the effectiveness and feasibility of the proposed approach and bring future research directions.

Blind spot sensor systems for power wheelchairs: obstacle detection accuracy, cognitive task load, and perceived usefulness among older adults

Purpose Blind spot sensor systems can improve power wheelchair (PWC) safety. This research (1) compared accuracy of obstacle detection in the rear of a wheelchair with and without a sensor system, and (2) explored cognitive task load and perceived usability, safety, confidence and awareness in a laboratory setting, and (3) PWC users’ perceptions in real-world settings. Materials and methods A mixed-method design was used. PWC users were provided with the sensor system. In laboratory accuracy of obstacle detection with and without a sensor system, cognitive task load and perceived usability, safety, confidence and awareness were evaluated. Participants then used the sensor system at home for two-months before completing semi-structured interviews. Statistical and thematic analyses were conducted. Results Among 11 PWC users (age = 67.5 ± 7.5y), obstacles were detected more accurately with sensor system than without (p < 0.001). Using the sensor system required lower cognitive task loads (p = 0.005). The system was perceived by most users as easy to use (9/11) and its capabilities meeting their requirements (8/11). Most users did not perceive safety (9/11), confidence (9/11) or increased awareness (10/11) in the laboratory. Three themes emerged in the follow-ups: perceived usefulness, barriers to use, and recommendations. Four participants reported continued use after 2 months, reporting perceived increased awareness, convenience, and independence using the system. Those who discontinued use reported perceived lack of usefulness and technical issues. Recommendations included types of users who can benefit and sensor improvements. Conclusions Sensor systems may improve obstacle detection accuracy while reducing cognitive task load. However, larger scale implementation should consider recommendations for PWC service provision. IMPLICATIONS FOR REHABILITATION Blind spot sensors systems increased speed and accuracy of obstacle detection when using a power wheelchair. Technical and hardware issues encountered by PWC users highlight the need for training and support services. Technical support was out of scope for the current research project and will be explored in future research given the critical role it might play in the usability and adoption of assistive technologies. PWC users perceived there to be practical uses for blind spot sensor systems.

Obstacles and Areas Detection Based on Pulse Width Modulation Method for Electric Wheelchair Safety Using Ultrasound Sensors

People with disabilities find it difficult to operate wheelchairs especially those with disabilities who do not have hands and feet or are disabled. The aim of this research is to improve safety control for electric wheelchairs by detecting obstacles and detecting areas that minimize collisions against obstacles. The contribution of this research is the use of the HC-SR04 ultrasound sensors to avoid a wheelchair colliding with an obstacle using the Pulse Width Modulation (PWM) method which is set based on the distance of the obstacle and the detection of free areas and corridor areas using the PWM method which is adjusted based on following the wall. The HC-SR04 ultrasound sensor is used as input and is processed on the Arduino nanomicrocontroller series to produce a value of PWM. Measuring the HC-SR04 ultrasound sensor against obstacles obtained the largest deviation of 0.72 and average 148,6 cm at a distance of 150 cm and the smallest deviation at a distance of less than 50 cm and the wheelchair can move at slow, medium and fast speeds with the measurement results on Duty Cycles of 71%, 82% and 94%. The results showed that the final distance of the wheelchair against obstacles was less than 50 cm and the wheelchair moved at a distance of 34 - 53 cm following the wall. The results of this study can control motor speed based on obstacles that can be implemented in electric wheelchairs to improve safety and ease of operation for people with disabilities.

Wheelchair satisfaction among elderly Arab and Jewish patients – a cross-sectional survey

Purpose To evaluate patient satisfaction with their wheelchairs among Jewish and Arab older adults. Materials and method A cross-sectional study based on personal interviews. Participants were confined to a wheelchair (WC) Level 3. The Quebec User Evaluation of Satisfaction (scales 1–5) evaluated patient satisfaction. T test evaluated differences between continuous variables and or a Fisher’s exact test between categorical variables. The contribution of background variables to the overall satisfaction with the wheelchair and related service was evaluated by multivariate regression. Results A total of 74 Jewish and 24 Arab (mean age 78.4 ± 14.1), participated in the study. The overall satisfaction score was moderate/high (3.97 ± 0.8) with no difference between the groups. The most important items for all participants were WC safety, comfort and weight. Participants from the Arab sector were less educated, had higher BMI and were less satisfied with the chair dimensions. Conclusions A WC Level 3 may be effective and meets most of the basic needs of both Jewish and Arab patients at older ages. Higher BMI may result in difficulty to adopt to WC dimensions and can explain the less satisfaction of Arab patients with this component. A routine initial phone call follow-up is recommended to all patients after receiving a wheelchair. Those with problems will be scheduled for home visit. It is recommended to perform a study that will include more types of assistive devices and other groups of patients. Implication For Rehabilitation Apparently, despite lack of home visit follow-up, overall satisfaction with wheelchaires is relatively high. Therefore, initial phone calls may be monitored to all patients and home visits may be scheduled only for those unsatisfied with their chair. Converting a home visit to phone call can reduce the costs associated with routine home visits for all patients as is currently the case in some counties. This counts for both Jewish and Arab patients.

Sudden stop detection and automatic seating support with neural stimulation during manual wheelchair propulsion

Objective: Wheelchair safety is of great importance since falls from wheelchairs are prevalent and often have devastating consequences. We developed an automatic system to detect destabilizing events during wheelchair propulsion under real-world conditions and trigger neural stimulation to stiffen the trunk to maintain seated postures of users with paralysis. Design: Cross-over intervention Setting: Laboratory and community settings Participants: Three able-bodied subjects and three individuals with SCI with previously implanted neurostimulation systems Interventions: An algorithm to detect wheelchair sudden stops was developed. This was used to randomly trigger trunk extensor stimulation during sudden stops events Outcome Measures: Algorithm success and false positive rates were determined. SCI users rated each condition on a seven-point Usability Rating Scale to indicate safety. Results: The system detected sudden stops with a success rate of over 93% in community settings. When used to trigger trunk neurostimulation to ensure stability, the implant recipients consistently reported feeling safer (P<.05 for 2/3 subjects) with the system while encountering sudden stops as indicated by a 1–3 point change in safety rating. Conclusion: These preliminary results suggest that this system could monitor wheelchair activity and only apply stabilizing neurostimulation when appropriate to maintain posture. Larger scale, unsupervised and longer-term trials at home and in the community are indicated. This system could be generalized and applied to individuals without an implanted stimulation by utilizing surface stimulation, or by actuating a mechanical restraint when necessary, thus allowing unrestricted trunk movements and only restraining the user when necessary to ensure safety. Trial Registration: NCT01474148

Effect of an mHealth Wheelchair Skills Training Program for Older Adults: A Feasibility Randomized Controlled Trial

ObjectiveTo evaluate the effect of an mHealth wheelchair skills training program on clinical outcomes among older adult manual wheelchair users. Design2×2 factorial randomized controlled trial. SettingCommunity setting in 2 Canadian cities. ParticipantsConvenience sample of manual wheelchair users 50 years and older living in the community who were able to self-propel with both hands and communicate in English. Participants (N=18) were randomized into either a mHealth treatment (n=10) or tablet gaming control (n=8) group. InterventionsAll participants received 2 in-person sessions with their trainer and engaged in a 4-week monitored home training program with a computer tablet. The Enhancing Participation In the Community by improving Wheelchair Skills program provided wheelchair skills training; the control program included 9 dexterity and cognitive training games. Main Outcome MeasuresThe primary outcome was wheelchair skill capacity. Secondary outcomes included safety, self-efficacy, activity participation, mobility, divided-attention, and health-related quality of life. ResultsData collection was blinded to group allocation. Capacity improved by 2 skills but with no statistically significant between-group difference. The mHealth training program had a significant effect on participation (P=.03) and self-efficacy (P=.06) with large effect sizes (ηp2=0.22-0.29). Mobility, safety with skill performance, and divided attention measures demonstrated medium effect size changes, but only safety with skill performance was statistically significant. The program was more beneficial for participants with <1 year of wheelchair experience. ConclusionEnhancing Participation In the Community by improving Wheelchair Skills participants demonstrated good program adherence and clinical benefits were evident in community participation and wheelchair self-efficacy. Wheelchair safety and mobility were positively affected, while skill capacity showed a small, nonsignificant improvement. Future study should investigate benefit retention over time.

EMG Sensor based Wheel Chair Control and Safety System

This work presents the development of a smart wheelchair, controlled and guided by EMG signals for those facing the problems of physical disabilities. Conventional wheelchairs were not effective for people with disabilities, as it requires a great pedalling power and gets affected by users’ limitations such as defects of the fingers. This project proposes a solution in the form of a 4-channel EMG controlled Electric wheelchair. The EMG signals are acquired using EMG sensors attached to the muscles of arms and are read in Arduino Uno board working with an AVR microcontroller. The battery powered wheelchair has 3 different modules- Signal acquisition Module, Signal Analysis Module and finally the Wheelchair controlled module. Unlike its previous versions, this wheelchair will move auto-forward whenever powered ON, moves right and left with the required muscle movements, and given further more delay on either of the directions it will turn backwards. Apart from this, an additional feature of designated stop switch is also included for ceasing wheelchair’s motion. Hence this project ensures the wheelchair's motion to be smoother.

Design of electric wheelchair controller based on brainwaves spectrum EEG sensor

The purpose of this study was to design of electric wheelchair. A wheelchair is a tool for people with disability. In general, a wheelchair is still controlled by hand or using a Joystick electrically, so users with a disorder can perform activities without any help. The research was designed to help disabilities who do not have hands and feet. So there needs to be wheelchair control without the use of hand or foot muscles. Therefore, it is designed an electric wheelchair that can be controlled based on brain wave activity using EEG sensors. EEG sensor is a device that can detect the activity of human brainwaves, this sensor can be used for control on a movement direction of a wheelchair, based on the parameters Blink detection and Attention. The laptop device is used as a LabVIEW-based programming control center that is associated with the Arduino as a communication between input and output components. As well as the ultrasonic distance sensor HCSR-04 is used as a wheelchair safety. Wheelchair movement can be controlled automatically, with the movement forward, backward, turn right and turn left, with an average success rate of 80%. This is because there is instability in detecting brain signals. Wheelchair ability in bringing user load is less than 130 kg.

An Intelligent Wheelchair Anti-Lock Braking System Design With Friction Coefficient Estimation

In the future, the population’s aging problem will become increasingly serious. For medical care, action-assisted vehicles are one of the solutions. A wheelchair is a common mobility assistance equipment, and the safety of the user is an important issue. For the ordinary wheelchairs, most of them use a fixed hand brake or disk brake to allow the users to stop the wheelchair. However, improper use of brakes tends to cause slippage, and there is no concept of the anti-lock braking system (ABS) for the wheelchair. Therefore, this paper applies this idea to intelligent wheelchairs with the aim of enhancing the safety of users in wheelchairs. The ABS architecture includes the core algorithms of the adaptive fuzzy-neural inference system and the friction coefficient estimation system. The friction coefficient estimation system uses a particle filter to quickly adapt to a non-linear state and unknown environment. The system provides more accurate braking control for the ABS according to the range of the friction coefficient based on the pavement type. In ABS, it uses the gyroscope to detect the acceleration and wheelchair angle information and then calculates the parameters of the wheelchair. The user clicks the stop command on the wheelchair to activate the brake system to achieve a simple and efficient mode of operation. It can efficiently reduce the braking time and braking distance and enhance the riding safety of wheelchairs.

Relationship between the Assessment of Abilities in Wheelchair Safety Operation Management and Prognosis of Walking Ability in Stroke Patients: A Comparison with the Academic Cognitive Function Test

Relationship between the Assessment of Abilities in Wheelchair Safety Operation Management and Prognosis of Walking Ability in Stroke Patients: A Comparison with the Academic Cognitive Function Test

Hazardous Object Detection by Using Kinect Sensor in a Handle-Type Electric Wheelchair.

To ensure the safety of a handle-type electric wheelchair (hereinafter, electric wheelchair), this paper describes the applicability of using a Kinect sensor. Ensuring the mobility of elderly people is a particularly important issue to be resolved. An electric wheelchair is useful as a means of transportation for elderly people. Considering that the users of electric wheelchairs are elderly people, it is important to ensure the safety of electric wheelchairs at night. To ensure the safety of an electric wheelchair at night, we constructed a hazardous object detection system using commercially available and inexpensive Kinect sensors and examined the applicability of the system. We examined warning timing with consideration to the cognition, judgment, and operation time of elderly people. Based on this, a hazardous object detection area was determined. Furthermore, the detection of static and dynamic hazardous objects was carried out at night and the results showed that the system was able to detect with high accuracy. We also conducted experiments related to dynamic hazardous object detection during daytime. From the above, it showed that the system could be applicable to ensuring the safety of the handle-type electric wheelchair.

A Wheelchair Design for Topple Prevention

Wheelchairs are an indispensable transportation tool for the aged and for the disabled. Tips and falls are common accidents to current wheelchairs, suggesting wheelchair safety is imperfect. This study thus proposed an anti-tip concept design to improve manual wheelchair safety. The anti-tip wheelchair was ergonomically designed with the following features: (1) diamond pattern wheel alignment enables users to adjust rear-wheel chamber angle and extend the front and rear casters forward and backward respectively to prevent tipping by increasing the base area; (2) adjustable wheel mechanism for indoor and outdoor uses; (3) collinear alignment of the rear-wheel axle and the center of mass in the sagittal plane reduces push frequency and enhances push power; (4) a spring-driven rear caster compression mechanism aids passing over obstacles on the ground; and (5) height adjustable neck rest provides comfort support.

RESNA's Position on Wheelchairs Used as Seats in Motor Vehicles

ABSTRACT This position paper is based on the premise that those who ride seated in wheelchairs are entitled to equivalent occupant safety when they are traveling in motor vehicles. The document summarizes research and best practice for safety and selection of crashworthy wheelchairs with the requisite features required by the WC19 safety standard when it is necessary for individuals to use a wheelchair as a seat in a motor vehicle. Recommendations are based on data from accident and injury databases, prior research and a synopsis of the design, testing, performance and labeling requirements of ANSI and ISO voluntary industry standards for wheelchair transportation safety. This paper is intended for an audience of consumers, rehabilitation and health care professionals, manufacturers of wheelchairs and wheelchair transportation equipment and those who make reimbursement and public policy decisions. Content reviewed by the RESNA SIG on Wheeled Mobility and modified in accordance with SIG reviewers comments—January 2010. Approved by the RESNA Board of Directors—April 2010.