A RGBD-Based interactive system for gaming-driven rehabilitation of upper limbs

Current physiotherapy services may not be effective or suitable for certain patients due to lack of motivation, poor adherence to exercises, insufficient supervision and feedback or, in the worst case, refusal to continue with the rehabilitation plan. This paper introduces a novel approach for rehabilitation of upper limbs through KineActiv, a platform based on Microsoft Kinect v2 and developed in Unity Engine. KineActiv proposes exergames to encourage patients to perform rehabilitation exercises prescribed by a specialist, controls the patient's performance, and corrects execution errors on the fly. KineActiv comprises a web platform where the physiotherapist can review session results, monitor patient health, and adjust rehabilitation routines. We recruited 10 patients for assessing the system usability as well as the system performance. Results show that KineActiv is a usable, enjoyable and reliable system, that does not cause any negative feelings

Automating senior fitness testing through gesture detection with depth sensors

Sedentarism has a negative impact on health, life expectancy and quality of life, especially in older adults. The assessment of functional fitness helps evaluating the effects of ageing and sedentarism, and this assessment is typically done through validated battery tests such as the Senior Fitness Test (SFT). In this paper we present a computer-based system for assisting and automating SFT administration and scoring in the elderly population. Our system assesses lower body strength, agility and dynamic balance, and aerobic endurance making use of a depth sensor for body tracking and multiple gesture detectors for the evaluation of movement execution. The system was developed and trained with optimal data collected in laboratory conditions and its performance was evaluated in a real environment with 22 elderly end-users, and compared to traditional SFT administered by an expert. Results show a high accuracy of our system in identifying movement patterns (>95%) and consistency with the traditional fitness assessment method. Our results suggest that this technology is a viable low cost option to assist in the fitness assessment of elderly that could be deployed for at home use in the context of fitness programs.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

深度センサーを用いた脳卒中後の運動機能の自動評価システム開発に関する研究

脳卒中後の身体機能の評価に用いられる脳卒中機能評価法(SIAS) は，臨床において，特殊な器具を使わずに判定できる言語ルールベースの基準を用いて評価する。ところが，その基準は，曖昧な言語表現で書かれており，目測による評価であることから，その判定が曖昧になり，恣意性が含まれがちになる。そのため，同一の被験者に対して異なる結果が生じることもあり，それを回避するためには，定量的に評価するシステムが必要である。実際，モーションキャプチャーシステムを使って定量的な評価の試みもなされているが，利用者や使用者の負担が大きく，日常的に臨床で用いるのは困難である。臨床において，理学療法士による曖昧性を含む言語的ルールに基づいた評価判定方法は，すでに日常的に使用されており，自動的に，一意的に評価するような新たなシステムに置き換えることは困難である。よって，システムを構築するにあたり，理学療法士一人ひとりが，目視による計測・判定に近づけるために調整を行うことのできるパラメータを具備する必要がある。そこで，本研究では，安価な深度センサー類，特にKinect やLeapMotion を用いて，日常的に用いられる新たなSIAS の定量的な評価システムを構築する。ここで開発するSIAS の評価システムについて，(1) Kinect の関節検知機能，(2) LeapMotion の関節検知機能，を用いるものと，(3) 深度画像から身体の特徴部位等を検出するアルゴリズムを作りこみ，それを用いて評価するものに分ける。(1) では，麻痺側運動機能に含まれる膝口テスト，股関節屈曲テスト，膝関節伸展テスト，足パットテスト，(2) では，手指テスト，視空間認知検査，(3) では，体幹機能に含まれる腹筋力テストと垂直性テスト，関節可動域測定に含まれる肩関節と足関節について，角度計測値を用いた評価システムが含まれる。このシステムを用いて，身体に問題のない若年成人者を対象に実験を行い，また，実際の対象者となる高齢者や片麻痺者に対する実証実験も同時に行った。その結果，本システムと理学療法士とが評価した結果を比較したところ，高い一致率を示した。理学療法士をはじめとする専門家が各々の判定基準を持ちながら評価する際にも，本研究で開発したシステムを併用して，本システムが出力する数値データによる評価の裏付けが可能である。このことから，本システムは新たに提案できる定量的な評価システムのプロトタイプになると考える。Stroke Impairment Assessment Set (SIAS) is used to evaluate bodily function after stroke. In daily clinical treatment, SIAS is evaluated by using fuzzy linguistic rules without special equipment, which tends to include personal arbitrariness. This may lead to different results among physical therapists for a same client. A quantitative evaluation systems are required to avoid difference evaluations between testers. In fact, motion capture systems have been applied for quantitative measurement methods, but it costs expensive and forces troublesome tasks to clients and operators.SIAS is conducted with linguistic fuzzy rules, thus it is difficult to change it to automatic unified evaluation methods even if it exists. Therefore, it is necessary to develop systems with changeable parameters to adjust each physical therapist. In this study, a new quantitative evaluation system is developed by using low-cost portable depth sensors such as Kinect and Leap Motion for SIAS.Here, systems with three categories are developed: (1) Kinect applications using body joint detection function, (2) Leap Motion applications for finger detection, and (3) depth sensor applications by finding the feature of the body shape that cannot be detected properly by the joint detection. In (1), algorithms for testing paralysis motor functions are developed including knee mouth test, hip flexion test, knee extension test and foot pat test by using joint detected function. In (2), for finger test and visuospatial test systems are developed. In (3), evaluation systems are developed for trunk function inspection in abdominal test and vertical tests, and range of motion inspection in shoulder and ankle joints, by using depth data.Experimental study conducted with healthy young persons, elderly persons and hemiplegia persons. The results of experiments show that the measurement values and the judgements were very similar between the results by this system and physical therapists. Even when the SIAS is tested by physical therapists as the traditional way, it is possible for this system to supply various data to strengthen the judgement. In this way, this prototype can also be applied to various quantitative evaluation methods other than SIAS.甲南大学令和元年度(2019年度

Enabling real-time automatic assessment of patient exercises for technology-assisted physical rehabilitation interventions

Technology-assisted physical rehabilitation interventions (TAPRI) have the potential to offer patients a safe, motivating and always accessible platform for undergoing rehabilitation. The emergence of compact and affordable depth sensors provide an opportunity to realise such interventions in a home environment. These types of depth sensors can run pose estimation algorithms that track full-body human joint positions in real-time. TAPRIs that provide real-time patient performance assessment and feedback require sufficiently accurate algorithms to ensure a correct assessment. The research presented in this thesis aims to overcome some of the algorithmic challenges in enabling real-time patient performance assessment and feedback. This research focuses on two algorithms: real-time tracking of human joint positions and real-time segmentation of exercise repetitions. This research targets stroke rehabilitation as a challenging use case for achieving real-time patient exercise assessment as stroke patients often have varying levels of mobility. Research contributions. The first contribution of this thesis is a quantitative and clinical evaluation of a state-of-the-art pose estimation algorithm (human joint tracking) to determine if the joint position estimations are sufficiently accurate for correctly assessing stroke rehabilitation exercises. This evaluation also determines what the limitations are and propose recommendations for future pose estimation algorithms intended for clinical applications. The second contribution is an evaluation of the inter-rater reliability of clinicians assessing the suitability of the pose estimation algorithm, to quantitatively determine where the clinicians are in agreement and propose more robust criteria for the assessment of new clinical technologies. The final contribution is the proposal of a real-time segmentation algorithm that requires only a single exemplar repetition of an exercise to segment repetitions from other subjects, including those with impaired mobility. Main research findings and results. The accuracy of current state-of-the-art pose estimation algorithms are insufficient for correctly assessing patient performance. There was a low inter-rater agreement between clinicians evaluating the accuracy of the individual joints of a state-of-the-art pose estimation algorithm, however overall the accuracy was found to be insufficient. Our proposed segmentation algorithm correctly segments 90% of stroke patient exercise repetitions from our own rehabilitation exercise dataset and is capable of segmenting a 20 second window at 30Hz in real-time on a desktop computer