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Advances in artificial intelligence (AI) have made it increasingly applicable to supplement expert’s decision-making in the form of a decision support system on various tasks. For instance, an AI-based system can provide therapists quantitative analysis on patient’s status to improve practices of rehabilitation assessment. However, there is limited knowledge on the potential of these systems. In this paper, we present the development and evaluation of an interactive AI-based system that supports collaborative decision making with therapists for rehabilitation assessment. This system automatically identifies salient features of assessment to generate patient-specific analysis for therapists, and tunes with their feedback. In two evaluations with therapists, we found that our system supports therapists significantly higher agreement on assessment (0.71 average F1-score) than a traditional system without analysis (0.66 average F1-score, p

Technology-assisted systems to monitor and assess rehabilitation exercises have an opportunity of enhancing rehabilitation practices by automatically collecting patient's quantitative performance data. However, even if a complex algorithm (e.g. Neural Network) is applied, it is still challenging to develop such a system due to patients with various physical conditions. The system with a complex algorithm is limited to be a black-box system that cannot provide explanations on its predictions. To address these challenges, this paper presents a hybrid model that integrates a machine learning (ML) model with a rule-based (RB) model as an explainable artificial intelligence (AI) technique for quantitative assessment of stroke rehabilitation exercises. For evaluation, we collected therapist's knowledge on assessment as 15 rules from interviews with therapists and the dataset of three upper-limb stroke rehabilitation exercises from 15 post-stroke and 11 healthy subjects using a Kinect sensor. Experimental results show that a hybrid model can achieve comparable performance with a ML model using Neural Network, but also provide explanations on a model prediction with a RB model. The results indicate the potential of a hybrid model as an explainable AI technique to support the interpretation of a model and fine-tune a model with user-specific rules for personalization.

Automated assessment of rehabilitation exercises using machine learning has a potential to improve current rehabilitation practices. However, it is challenging to completely replicate therapist's decision making on the assessment of patients with various physical conditions. This paper describes an interactive machine learning approach that iteratively integrates a data-driven model with expert's knowledge to assess the quality of rehabilitation exercises. Among a large set of kinematic features of the exercise motions, our approach identifies the most salient features for assessment using reinforcement learning and generates a user-specific analysis to elicit feature relevance from a therapist for personalized rehabilitation assessment. While accommodating therapist's feedback on feature relevance, our approach can tune a generic assessment model into a personalized model. Specifically, our approach improves performance to predict assessment from 0.8279 to 0.9116 average F1-scores of three upper-limb rehabilitation exercises (p

The possibility of using serious games to stimulate people with dementia (PwD) has gained much attention in recent years. However, most of such games are not adapted to individual needs of such population in terms of the design of technology and its content. Thus, the desired therapeutic outcomes may not be achieved. Alternatively, more traditional approaches, such as the usage of music and reminiscence, have been shown to be able to lead to positive outcomes. Here, we propose a framework for serious games that allows healthcare professionals to customize music and reminiscence-based activities to stimulate PwD. It runs on an augmented reality setup, but also on PC, interactive table and tablet. Results from a usability study show that participants (1) were efficient in using the framework, (2) therapists are very interested in using it for stimulation purposes in PwD and (3) the usage of the framework was adequate in terms of effort and workload for PwD. Future deployments will be discussed in this article regarding the usage of the framework.

Due to the limited number of therapists, task-oriented exercises are often prescribed for post-stroke survivors as in-home rehabilitation. During in-home rehabilitation, a patient may become unmotivated or confused to comply prescriptions without the feedback of a therapist. To address this challenge, this paper proposes an automated method that can achieve not only qualitative, but also quantitative assessment of stroke rehabilitation exercises. Specifically, we explored a threshold model that utilizes the outputs of binary classifiers to quantify the correctness of a movements into a performance score. We collected movements of 11 healthy subjects and 15 post-stroke survivors using a Kinect sensor and ground truth scores from primary and secondary therapists. The proposed method achieves the following agreement with the primary therapist: 0.8436, 0.8264, and 0.7976 F1-scores on three task-oriented exercises. Experimental results show that our approach performs equally well or better than multi-class classification, regression, or the evaluation of the secondary therapist. Furthermore, we found a strong correlation (R2 = 0.95) between the sum of computed exercise scores and the Fugl-Meyer Assessment scores, clinically validated motor impairment index of post-stroke survivors. Our results demonstrate a feasibility of automatically assessing stroke rehabilitation exercises with the decent agreement levels and clinical relevance.

In recent years, Brain-Computer Interfaces (BCIs) have been steadily gaining ground in the market, used either as an implicit or explicit input method in computers for accessibility, entertainment or rehabilitation. Past research in BCI has heavily neglected the human aspect in the loop, focusing mostly in the machine layer. Further, due to the high cost of current BCI systems, many studies rely on low-cost and low-quality equipment with difficulties to provide significant advancements in physiological computing. Open-Source projects are offered as alternatives to expensive medical equipment. Nevertheless, the effectiveness of such systems over their cost is still unclear, and whether they can deliver the same level of experience as their more expensive counterparts. In this paper, we demonstrate that effective BCI interaction in a Motor-Imagery BCI paradigm can be accomplished without requiring high-end/high-cost devices, by analyzing and comparing EEG systems ranging from open source devices to medically certified systems.

Worldwide, more than one in three adults suffers from a cardiovascular disease. According to the World Health Organization, 15 million people experience a stroke each year and, of these, 5 million stay permanently disabled. The current limitations of traditional rehabilitation methods push towards the design of personalized tools that can be used intensively by patients and therapists in clinical or at-home environments. In this paper we present the design, implementation and validation of RehabCity, an online game designed for the rehabilitation of cognitive deficits through a gamified approach on activities of daily living (ADLs). Among other findings, our results show a strong correlation between the RehabCity scoring system and the Mini Mental State Examination test for clinical assessment of cognitive function in several domains. These findings suggest that RehabCity is a valid tool for the quantitative assessment of patients with cognitive deficits derived from a brain lesion.