Interactive hybrid approach to combine machine and human intelligence for personalized rehabilitation assessment

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 deci sion 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 ex pert’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 rehabilita tion assessment. While accommodating therapist’s feedback on fea ture 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 ( < 0.01). Our work demonstrates that machine learning models with feature selection can generate kinematic feature-based analysis as expla nations on predictions of a model to elicit expert’s knowledge of assessment, and how machine learning models can augment with expert’s knowledge for personalized rehabilitation assessment.info:eu-repo/semantics/publishedVersio

A framework for user adaptation and profiling for social robotics in rehabilitation

Physical rehabilitation therapies for children present a challenge, and its success—the improvement of the patient’s condition—depends on many factors, such as the patient’s attitude and motivation, the correct execution of the exercises prescribed by the specialist or his progressive recovery during the therapy. With the aim to increase the benefits of these therapies, social humanoid robots with a friendly aspect represent a promising tool not only to boost the interaction with the pediatric patient, but also to assist physicians in their work. To achieve both goals, it is essential to monitor in detail the patient’s condition, trying to generate user profile models which enhance the feedback with both the system and the specialist. This paper describes how the project NAOTherapist—a robotic architecture for rehabilitation with social robots—has been upgraded in order to include a monitoring system able to generate user profile models through the interaction with the patient, performing user-adapted therapies. Furthermore, the system has been improved by integrating a machine learning algorithm which recognizes the pose adopted by the patient and by adding a clinical reports generation system based on the QUEST metricThis work is partially funded by grant RTI2018-099522-B-C43 of FEDER/Ministerio de Ciencia e Innovación - Ministerio de Universidades - Agencia Estatal de Investigació

Motion Capture for Telemedicine: A Review of Nintendo Wii, Microsoft Kinect, and PlayStation Move

Access to healthcare has been and continues to be difficult for many around the world. With the introduction of telemedicine, this impediment to attaining medical care has been lifted. Although many avenues of telemedicine exist (and have yet to exist), the use of home video game consoles such as the Nintendo Wii®, Microsoft Kinect®, and PlayStation Move® can be used to measure patient progress outside of the office. Due to the nature of each individual console/system, some unique characteristics exist that allow each system to provide its own clinical potential. A comparative analysis of the clinical implications of the Nintendo Wii®, Microsoft Kinect®, and PlayStation Move® showed that with its ease of use and dynamic accuracy, the Microsoft Kinect® offered the most benefit. With further exploration, using the Microsoft Kinect® for telemedicine will be able to improve medical efficiency and hopefully health outcomes

An objective evaluation method for rehabilitation exergames

The aim of this work is to objectively evaluate the performance of patients using a virtual rehabilitation system called MIRA. MIRA is a software platform which converts conventional therapeutic exercises into games, enabling the user to practice the given exercise by playing a game. The system includes a motion sensor to track and capture user's movements. Our assessment of the performance quality is based on the recorded trajectories of the human skeleton joints. We employ two different machine learning approaches, dynamic time warping (DTW) and hidden Markov modeling (HMM), both widely used for gesture recognition, to compare the user's performance with that of a reference as ground truth

A Fuzzy Logic Architecture for Rehabilitation Robotic Systems

Robots are highly incorporated in rehabilitation in the last decade to compensate lost functions in disabled individuals. By controlling the rehabilitation robots from far, many benefits are achieved. These benefits include but not restricted to minimum hospital stays, decreasing cost, and increasing the level of care. The main goal of this work is to have an effective solution to take care of patients from far. Tackling the problem of the remote control of rehabilitation robots is undergoing and highly challenging. In this paper, a remote wrist rehabilitation system is presented. The developed system is a sophisticated robot ensuring the two wrist movements (Flexion /extension and abduction/adduction). Additionally, the proposed system provides a software interface enabling the physiotherapists to control the rehabilitation process remotely. The patient’s safety during the therapy is achieved through the integration of a fuzzy controller in the system control architecture. The fuzzy controller is employed to control the robot action according to the pain felt by the patient. By using fuzzy logic approach, the system can adapt effectively according to the patients’ conditions. The Queue Telemetry Transport Protocol (MQTT) is considered to overcome the latency during the human robot interaction. Based on a Kinect camera, the control technique is made gestural. The physiotherapist gestures are detected and transmitted to the software interface to be processed and be sent to the robot. The acquired measurements are recorded in a database that can be used later to monitor patient progress during the treatment protocol. The obtained experimental results show the effectiveness of the developed remote rehabilitation system