This work describes a new human-in-the-loop (HitL) assistive grasping system for individuals with varying levels of physical capabilities. We investigated the feasibility of using four potential input devices with our assistive grasping system interface, using able-bodied individuals to define a set of quantitative metrics that could be used to assess an assistive grasping system. We then took these measurements and created a generalized benchmark for evaluating the effectiveness of any arbitrary input device into a HitL grasping system. The four input devices were a mouse, a speech recognition device, an assistive switch, and a novel sEMG device developed by our group that was connected either to the forearm or behind the ear of the subject. These preliminary results provide insight into how different interface devices perform for generalized assistive grasping tasks and also highlight the potential of sEMG based control for severely disabled individuals.
In order to evaluate the effectiveness of this system we used a pick and place task that was developed as a modification of the Box and Blocks Test of manual dexterity @cite_8 . This test is used to evaluate physically handicapped individuals to develop a normative metric for adults. We believe a test such as this is appropriate for evaluating whether the system developed is achieving a useful and or meaningful task. We opted for this instead of the Action Research Arm Test (ARAT) because it requires less than 1cm of noise for detecting the objects to be manipulated in that test and the equipment required for such precise point cloud measurements is not cost effective for an ordinary user at the time of this writing.
@cite_8
1977086599 2785342388 2160770229 2113096211
The Box and Block Test, a test of manual dexterity, has been used by occupational therapists and others to evaluate physically handicapped individuals. Because the test lacked normative data for adults, the results of the test have been interpreted subjectively. The purpose of this study was to develop normative data for adults. Test subjects were 628 Normal adults (310 males and 318 females) from the seven-county Milwaukee area. Data on males and females 20 to 94 years old were divided into 12 age groups. Means, standard deviations, standard error, and low and high scores are reported for each five-year age group. These data will enable clinicians to objectively compare a patient's score to a normal population parameter. Abstract This paper proposes the use of a non-immersive virtual reality rehabilitation system "ReHabgame" developed using Microsoft Kinect™ and the Thalmic™ Labs Myo gesture control armband. The ReHabgame was developed based on two third-person video games that provide a feasible possibility of assessing postural control and functional reach tests. It accurately quantifies specific postural control mechanisms including timed standing balance, functional reach tests using real-time anatomical landmark orientation, joint velocity, and acceleration while end trajectories were calculated using an inverse kinematics algorithm. The game was designed to help patients with neurological impairment to be subjected to physiotherapy activity and practice postures of daily activities. The subjective experience of the ReHabgame was studied through the development of an Engagement Questionnaire (EQ) for qualitative, quantitative and Rasch model. The Monte-Carlo Tree Search (MCTS) and Random object (ROG) generator algorithms were used to adapt the physical and gameplay intensity in the ReHabgame based on the Motor Assessment Scale (MAS) and Hierarchical Scoring System (HSS). Rasch analysis was conducted to assess the psychometric characteristics of the ReHabgame and to identify if these are any misfitting items in the game. Rasch rating scale model (RSM) was used to assess the engagement of players in the ReHabgame and evaluate the effectiveness and attractiveness of the game. The results showed that the scales assessing the rehabilitation process met Rasch expectations of reliability, and unidimensionality. Infit and outfit mean squares values are in the range of (0.68–1.52) for all considered 16 items. The Root Mean Square Residual (RMSR) and the person separation reliability were acceptable. The item person map showed that the persons and items were clustered symmetrically. In recent years, the field of Human-Computer Interaction (HCI) has been advanced with many technologies, however, most are limited to healthy users. In this paper, we leveraged the technology of free-hand interaction to rehabilitate patients with stroke. We modified the game of Fruit Ninja to use Leap Motion controller's hand tracking data for stroke patients with arm and hand weakness to practice their finger individuation. In a pilot study, we recruited 14 patients with chronic stroke to play the game using natural interaction. Their Fruit Ninja (FN) scores show high correlation with the standard clinical assessment scores such as Fugl-Meyer (FMA) and Box-and-Blocks Test (BBT) scores. This finding suggests that our free-hand Fruit Ninja's score is a good indicator of the patient's hand function and therefore will be informative if used in their rehabilitation. This paper presents a real-time vision-based system to assist a person with dementia wash their hands. The system uses only video inputs, and assistance is given as either verbal or visual prompts, or through the enlistment of a human caregiver's help. The system combines a Bayesian sequential estimation framework for tracking hands and towel, with a decision-theoretic framework for computing policies of action. The decision making system is a partially observable Markov decision process, or POMDP. Decision policies dictating system actions are computed in the POMDP using a point-based approximate solution technique. The tracking and decision making systems are coupled using a heuristic method for temporally segmenting the input video stream based on the continuity of the belief state. A key element of the system is the ability to estimate and adapt to user psychological states, such as awareness and responsiveness. We evaluate the system in three ways. First, we evaluate the hand-tracking system by comparing its outputs to manual annotations and to a simple hand-detection method. Second, we test the POMDP solution methods in simulation, and show that our policies have higher expected return than five other heuristic methods. Third, we report results from a ten-week trial with seven persons moderate-to-severe dementia in a long-term care facility in Toronto, Canada. The subjects washed their hands once a day, with assistance given by our automated system, or by a human caregiver, in alternating two-week periods. We give two detailed case study analyses of the system working during trials, and then show agreement between the system and independent human raters of the same trials.