Autonomous wheelchair for patient's transportation on healthcare institutions

Safety and feasibility of in-hospital autonomous transportation using a driverless mobility for patients with musculoskeletal disorders: preliminary clinical study to achieve mobility as a service in medical care

BACKGROUND

Recent advancements in and the proliferation of autonomous mobility technology, such as intelligent wheelchairs, have made it possible to provide mobility services for patients with reduced mobility due to musculoskeletal disorders. In the present study, we conducted a preliminary clinical study to assess the safety and feasibility of in-hospital autonomous transportation using a driverless mobility (wheelchair) for patients with musculoskeletal disorders.

METHODS

From January to February 2022, 51 patients with musculoskeletal disorders exhibiting gait disturbance who presented to our institution were included in the present study. Driverless mobility rides were conducted over a straight-line distance of 100 m from the orthopaedic outpatient reception to the payment counter after the outpatient consultation. We assessed the quality of life using an EQ-5D-5 L index and pain using a VAS score before riding the mobility to investigate the patient's condition. After the ride, a questionnaire survey was conducted to assess patient satisfaction on a 5-point scale. In addition, adverse events during the mobility ride were investigated.

RESULTS

Overall satisfaction levels showed that 44 out of 51 (86%) patients rated the level as 3 or higher. There were no significant differences in the level of satisfaction based on the cause of disorders or EQ-5D-5 L Index. Among 19 patients who rated the level of satisfaction as 2-3, the ratio of postoperative patients and those with pain tended to be higher (p < 0.05). While 26 of 51 (51%) patients reported moments of feeling unsafe during the mobility ride, no actual adverse events, such as collisions, were observed.

CONCLUSIONS

An in-hospital autonomous transportation service using a driverless mobility for patients with musculoskeletal disorders demonstrated high satisfaction levels and was safe with no severe adverse events observed. The expansion of autonomous mobility deployment is expected to achieve mobility as a service in medical care.

An autonomous wheelchair with health monitoring system based on Internet of Thing

Assistive powered wheelchairs will bring patients and elderly the ability of remain mobile without the direct intervention from caregivers. Vital signs from users can be collected and analyzed remotely to allow better disease prevention and proactive management of health and chronic conditions. This research proposes an autonomous wheelchair prototype system integrated with biophysical sensors based on Internet of Thing (IoT). A powered wheelchair system was developed with three biophysical sensors to collect, transmit and analysis users' four vital signs to provide real-time feedback to users and clinicians. A user interface software embedded with the cloud artificial intelligence (AI) algorithms was developed for the data visualization and analysis. An improved data compression algorithm Minimalist, Adaptive and Streaming R-bit (O-MAS-R) was proposed to achieve a higher compression ratio with minimum 7.1%, maximum 45.25% compared with MAS algorithm during the data transmission. At the same time, the prototype wheelchair, accompanied with a smart-chair app, assimilates data from the onboard sensors and characteristics features within the surroundings in real-time to achieve the functions including obstruct laser scanning, autonomous localization, and point-to-point route planning and moving within a predefined area. In conclusion, the wheelchair prototype uses AI algorithms and navigation technology to help patients and elderly maintain their independent mobility and monitor their healthcare information in real-time.

A Multimodal A* Algorithm to Solve the Two-Dimensional Optimization Problem of Accompanying a Person for an Intelligent Wheelchair

Impaired mobility have far-reaching consequences for handicapped persons and their relatives. Mobile robotic technologies enable intelligent wheelchairs to regain mobility for those affected. Multiple research projects address human aware navigation and the task of following a person for assistive robots. But just a few projects focus on accompanying a person to enable social interaction. Therefore, we present a navigation system for indoor navigation in dynamic and cluttered environments as well as a novel algorithm for accompanying a person.First, we developed an autonomous driving wheelchair for indoor navigation based on the robot operating system (ROS). Thereby, a multi sensor setup using cameras and laser scanner enables localization within a map. People are detected by the same sensors and tracked by a Kalman filter. Afterwards we propose a novel algorithm to achieve a dynamic accompanying behaviour. An attractiveness distribution is introduced to evaluate the possible accompanying positions next to the manually selected target person regarding social interaction. The resulting two-dimensional optimization problem is solved by a novel multimodal extension of the A* algorithm.The proposed intelligent wheelchair is able to navigate in indoor environments and to accompany any person. In addition it allows social interaction while walking to relieve relatives or nursing staff, which otherwise need to push the wheelchair. The aim is to increase participation in everyday life for those affected.

Application of Neuroengineering Based on EEG Features in the Industrial Design of Comfort

The smart wheelchair is a service robot that can be used as a means of transportation for the elderly and the disabled. The patients were given an intelligent wheelchair designed by electroencephalogram (EEG), which was used for more than 8 hours and tested continuously for 1 month. By ridit analysis, the difference between the two groups was statistically significant (U = 3.72, P < 0.01). The scores of visual analogue scale (VAS) and joint ground visuality (JGV) in the observation group were significantly better than those in the control group. The modules of physiological function (PF), physical pain (PP), overall health (OH), vitality (VT), social function (SF), emotional function (EF), and mental health (MH) in the SF-36 scores of the two groups were significantly improved (P < 0.05), and the improvement of each module in the observation group was significantly better than that in the control group (P < 0.05). The levels of serum IL-6, IL-10, and superoxide dismutase (SOD) in the two groups were significantly improved (P < 0.05), and the improvement of serum IL-6, IL-10, and SOD in the observation group was significantly better than that in the control group (P < 0.05). It is suggested that neural engineering based on EEG characteristics can be well applied in comfort industrial design.

Development of an Autonomous Driving Smart Wheelchair for the Physically Weak

People who have difficulty moving owing to problems in walking spend their lives assisted by wheelchairs. In the past, research has been conducted regarding the application of various technologies to electric wheelchairs for user convenience. In this study, we evaluated a method of applying an autonomous driving function and developed an autonomous driving function using ROS. An electric wheelchair with a control unit designed to enable autonomous driving was used to test the basic performance of autonomous driving. The effectiveness of the technology was confirmed by comparing the results of autonomous driving with those of manual driving on the same route. It is expected that the evaluation and improvement of the usability and ride quality as well as additional studies will help improve the mobility convenience of physically disabled persons.