The development of an adaptive upper-limb stroke rehabilitation robotic system

Abstract

BackgroundStroke is the primary cause of adult disability. To support this large population in recovery, robotic technologies are being developed to assist in the delivery of rehabilitation. This paper presents an automated system for a rehabilitation robotic device that guides stroke patients through an upper-limb reaching task. The system uses a decision theoretic model (a partially observable Markov decision process, or POMDP) as its primary engine for decision making. The POMDP allows the system to automatically modify exercise parameters to account for the specific needs and abilities of different individuals, and to use these parameters to take appropriate decisions about stroke rehabilitation exercises.MethodsThe performance of the system was evaluated by comparing the decisions made by the system with those of a human therapist. A single patient participant was paired up with a therapist participant for the duration of the study, for a total of six sessions. Each session was an hour long and occurred three times a week for two weeks. During each session, three steps were followed: (A) after the system made a decision, the therapist either agreed or disagreed with the decision made; (B) the researcher had the device execute the decision made by the therapist; (C) the patient then performed the reaching exercise. These parts were repeated in the order of A-B-C until the end of the session. Qualitative and quantitative question were asked at the end of each session and at the completion of the study for both participants.ResultsOverall, the therapist agreed with the system decisions approximately 65% of the time. In general, the therapist thought the system decisions were believable and could envision this system being used in both a clinical and home setting. The patient was satisfied with the system and would use this system as his/her primary method of rehabilitation.ConclusionsThe data collected in this study can only be used to provide insight into the performance of the system since the sample size was limited. The next stage for this project is to test the system with a larger sample size to obtain significant results.

People, sensors, decisions

“…The customization interface in this case gives the therapists access to only these simple parameters, and they set the amounts of stretch at which they expect a user to be able to reach the target, and how quickly a user will get fatigued. More details can be found in Kan et al [2008Kan et al [ , 2011.…”

“…The system consists of a haptic robotic device coupled to a POMDP model that tracks a user's progress over time, and adjusts the level of difficulty based on the user's current abilities. More details on this system can be found in Kan et al [2008Kan et al [ , 2011, Lam et al [2008], Huq et al [2011], and Goetschalckx et al [2011].…”

“…The responses were 2.8 ± 0.4 for question (a) and 3.2 ± 0.4 for question (b). More details can be found in Kan et al [2011]. We have also experimented with more complex, continuous models of how a person's abilities change with the task difficulty ].…”

Development of a robotic device for upper limb stroke rehabilitation: A user-centered design approach

“…An intuitive graphical user interface (GUI) for both therapists and stroke survivors and engaging rehabilitation games are currently being developed and evaluated by therapists. Work on an artificially intelligent controller capable of adapting to the rehabilitation needs of stroke survivors is in progress [28].…”

Sensor-based human activity recognition system with a multilayered model using time series shapelets

“…The maturity of the miniaturized sensors and supporting technologies has 5 pushed the research focus on context-aware triggered activity recognition and inference for a number of real-world applications, such as home monitoring and assisted living [2, 3,4], smart hospitals [5,6], rehabilitation [7,8], physical and sport activities [9,10,11], terrorist detection [12], and so forth. Particularly, the prevalence of mobile devices, such as the smartphone, equipped with powerful sensors and high-speed processors, can offer advanced capabilities to recognize human activity for developing smartphone-based healthcare and wellbeing applications [13,14].…”