Hip Lift Transfer Assistive System for Reducing Burden on Caregiver's Waist.

In Japan, the aging population is expected to increase the number of elderly people in the future. The purpose of this study was to develop a hip lift transfer assistive system to improve the QOL of elderly living and to prevent back pain for caregivers. We extracted the impediment factor and the necessary scene for the assistance, decided on the transfer process from the wheelchair in the toilet, and considered the reduction method of the burden based on the quantitative evaluation of the caregiver's lumbar burden and developed the device. Then we proposed the algorithm of the system by grasping the behavior and lumbar burden characteristics at the time of using the hip lift transfer assistive system by the developed device in which the proposed support algorithm of standing seating assistance operation is implemented in the actual use environment. Through the assistive movement evaluation experiment and the actual operation in the toilet, we have verified that the use of this device can reduce the caregivers' lumbar burden below the standard value (3400 N) and have proved the effectiveness of the proposed transfer assistive system.

Weakly-Supervised Recommended Traversable Area Segmentation Using Automatically Labeled Images for Autonomous Driving in Pedestrian Environment with No Edges.

Detection of traversable areas is essential to navigation of autonomous personal mobility systems in unknown pedestrian environments. However, traffic rules may recommend or require driving in specified areas, such as sidewalks, in environments where roadways and sidewalks coexist. Therefore, it is necessary for such autonomous mobility systems to estimate the areas that are mechanically traversable and recommended by traffic rules and to navigate based on this estimation. In this paper, we propose a method for weakly-supervised recommended traversable area segmentation in environments with no edges using automatically labeled images based on paths selected by humans. This approach is based on the idea that a human-selected driving path more accurately reflects both mechanical traversability and human understanding of traffic rules and visual information. In addition, we propose a data augmentation method and a loss weighting method for detecting the appropriate recommended traversable area from a single human-selected path. Evaluation of the results showed that the proposed learning methods are effective for recommended traversable area detection and found that weakly-supervised semantic segmentation using human-selected path information is useful for recommended area detection in environments with no edges.

Machine-learning approach to predict on-road driving ability in healthy older people.

AIM: In Japan, fatal traffic accidents due to older drivers are on the rise. Considering that approximately half the older drivers who have caused fatal accidents are cognitively normal healthy people, it has been required to detect older drivers who are cognitively normal but at high risk of having fatal traffic accidents. However, a standardized method for assessing the driving ability of older drivers has not yet been established. We thus aimed to identify a new sensing method for the evaluation of the on-road driving ability of healthy older people on the basis of vehicle behaviors. METHODS: We enrolled 33 healthy older individuals aged over 65 years and utilized a machine-learning approach to dissociate unsafe drivers from safe drivers based on cognitive assessments and a functional visual acuity test. RESULTS: The linear support vector machine classifier successfully dissociated unsafe drivers from safe drivers with accuracy of 84.8% (sensitivity of 66.7% and specificity of 95.2%). Five clinical parameters, namely age, the first trial of the Rey Auditory Verbal Learning Test immediate recall, the delayed recall of the Rey-Osterrieth Complex Figure Test, the result of the free-drawn Clock Drawing Test, and maximal visual acuity, were consistently selected as essential features for the best classification model. CONCLUSION: Our findings improve our understanding of clinical risk factors leading to unsafe driving and may provide insight into a new intervention that prevents fatal traffic accidents caused by healthy older people.

Development of an advanced mobile base for personal mobility and manipulation appliance generation II robotic wheelchair.

BACKGROUND: This paper describes the development of a mobile base for the Personal Mobility and Manipulation Appliance Generation II (PerMMA Gen II robotic wheelchair), an obstacle-climbing wheelchair able to move in structured and unstructured environments, and to climb over curbs as high as 8 inches. The mechanical, electrical, and software systems of the mobile base are presented in detail, and similar devices such as the iBOT mobility system, TopChair, and 6X6 Explorer are described. FINDINGS: The mobile base of PerMMA Gen II has two operating modes: "advanced driving mode" on flat and uneven terrain, and "automatic climbing mode" during stair climbing. The different operating modes are triggered either by local and dynamic conditions or by external commands from users. A step-climbing sequence, up to 0.2 m, is under development and to be evaluated via simulation. The mathematical model of the mobile base is introduced. A feedback and a feed-forward controller have been developed to maintain the posture of the passenger when driving over uneven surfaces or slopes. The effectiveness of the controller has been evaluated by simulation using the open dynamics engine tool. CONCLUSION: Future work for PerMMA Gen II mobile base is implementation of the simulation and control on a real system and evaluation of the system via further experimental tests.

White matter alterations in the dorsal attention network contribute to a high risk of unsafe driving in healthy older people.

Aim: Healthy older drivers may be at high risk of fatal traffic accidents. Our recent study showed that volumetric alterations in gray matter in the brain regions within the dorsal attention network (DAN) were strongly related to the risk of unsafe driving in healthy older people. However, the relationship between white matter (WM) structural connectivity and driving ability in healthy older people is still unclear. Methods: We used diffusion tensor imaging to examine the association between microstructural alterations in the DAN and the risk of unsafe driving among healthy older people. We enrolled 32 healthy older individuals aged over 65 years and screened unsafe drivers using an on-road driving test. We then determined the pattern of WM aberrations in unsafe drivers using tract-based spatial statistics. Results: The analysis demonstrated that unsafe drivers had significantly higher axial diffusivity values in nine WM clusters compared with safe drivers. These results were primarily observed bilaterally in the dorsal superior longitudinal fasciculus, which is involved in the DAN. Furthermore, correlation analyses showed that higher axial diffusivity values in the superior longitudinal fasciculus were associated with lower Trail Making Test A scores within unsafe drivers. This result suggests that functionally, WM microstructural alterations in the DAN are associated with attention problems, which may contribute to the risk of unsafe driving among healthy older people. Conclusion: Our findings may elucidate the neurobiological mechanisms underlying the increased risk of unsafe driving in healthy older people, potentially facilitating the development of new interventions to prevent fatal accidents.

Shared Control of an Electric Wheelchair Considering Physical Functions and Driving Motivation.

Individuals with severe physical impairments have difficulties operating electric wheelchairs (EWs), especially in situations where fine steering abilities are required. Automatic driving partly solves the problem, although excessive reliance on automatic driving is not conducive to maintaining their residual physical functions and may cause more serious diseases in the future. The objective of this study was to develop a shared control system that can be adapted to different environments by completely utilizing the operating ability of the user while maintaining the motivation of the user to drive. The operating characteristics of individuals with severe physical impairments were first analyzed to understand their difficulties when operating EWs. Subsequently, a novel reinforcement learning-based shared control method was proposed to adjust the control weight between the user and the machine to meet the requirements of fully exploiting the operating abilities of the users while assisting them when necessary. Experimental results showed that the proposed shared control system gradually adjusted the control weights between the user and the machine, providing safe operation of the EW while ensuring full use of the control signals from the user. It was also found that the shared control results were deeply affected by the types of users.

Regional Gray Matter Volume Identifies High Risk of Unsafe Driving in Healthy Older People.

In developed countries, the number of traffic accidents caused by older drivers is increasing. Approximately half of the older drivers who cause fatal accidents are cognitively normal. Thus, it is important to identify older drivers who are cognitively normal but at high risk of causing fatal traffic accidents. However, no standardized method for assessing the driving ability of older drivers has been established. We aimed to establish an objective assessment of driving ability and to clarify the neural basis of unsafe driving in healthy older people. We enrolled 32 healthy older individuals aged over 65 years and classified unsafe drivers using an on-road driving test. We then utilized a machine learning approach to distinguish unsafe drivers from safe drivers based on clinical features and gray matter volume data. Twenty-one participants were classified as safe drivers and 11 participants as unsafe drivers. A linear support vector machine classifier successfully distinguished unsafe drivers from safe drivers with 87.5% accuracy (sensitivity of 63.6% and specificity of 100%). Five parameters (age and gray matter volume in four cortical regions, including the left superior part of the precentral sulcus, the left sulcus intermedius primus [of Jensen], the right orbital part of the inferior frontal gyrus, and the right superior frontal sulcus), were consistently selected as features for the final classification model. Our findings indicate that the cortical regions implicated in voluntary orienting of attention, decision making, and working memory may constitute the essential neural basis of driving behavior.

One Dimensional Input Device of Electric Wheelchair for Persons with Severe Duchenne Muscular Dystrophy.

Persons with severe Duchenne Muscular Dystrophy (DMD) usually have difficult in operating electric wheelchairs (EW) using standard input device due to the lack of muscular power and the deformation of their hands. This paper proposed a novel one dimensional two degree of freedom (1D2F) input device based on the quantitative evaluation of hand function which consists of fingertip force and active range of motion. The validity and the operating features of this device are demonstrated by experiments.

Design and evaluation of a seat orientation controller during uneven terrain driving.

Electric powered wheelchairs (EPWs) are essential devices for people with disabilities as aids for mobility and quality of life improvement. However, the design of currently available common EPWs is still limited and makes it challenging for the users to drive in both indoor and outdoor environments such as uneven surfaces, steep hills, or cross slopes, making EPWs susceptible to loss of stability and at risk for falls. An alternative wheel-legged robotic wheelchair, "MEBot", was designed to improve the safety and mobility of EPW users in both indoor and outdoor environments. MEBot is able to elevate its six wheels using pneumatic actuators, as well to detect changes in the seat angle using a gyroscope and accelerometer. This capability enables MEBot to provide sensing for a dynamic self-leveling seat application that can maintain the center of mass within the boundaries of the wheelchair, thereby, improving EPW safety. To verify the effectiveness of the application, MEBot was tested on a motion platform with six degrees of freedom to simulate different slopes that could be experienced by the EPW in outdoor environments. The results demonstrate the robustness of the application to maintain the wheelchair seat in a horizontal reference against changes in the ground angle.

The Personal Mobility and Manipulation Appliance (PerMMA): a robotic wheelchair with advanced mobility and manipulation.

The Personal Mobility and Manipulation Appliance (PerMMA) is a recently developed personal assistance robot developed to provide people with disabilities and older adults enhanced assistance in both mobility and manipulation, which are two fundamental components for independently activities of daily life performing, community participation, and quality of life. Technologies to assist with mobility and manipulation are among the most important tools for clinicians, end users and caregivers; however, there are currently few systems that provide practical and coordinated assistance with mobility and manipulation tasks. The PerMMA was not only developed and evaluated to provide users and caregivers enhanced mobility and manipulation options, but also as a clinical tool as well as research platform. The development and evaluation of PerMMA are presented in the paper.