This article attempts to review papers on power assist rehabilitation robots, human motion intention, control laws, and estimation of power assist rehabilitation robots based on human motion intention in recent years. This paper presents the various ways in which human motion intention in rehabilitation can be estimated. This paper also elaborates on the control laws for the estimation of motion intention of the power assist rehabilitation robot. From the review, it has been found that the motion intention estimation method includes: Artificial Intelligence-based motion intention and Model-based motion intention estimation. The controllers include hybrid force/position control, EMG control, and adaptive control. Furthermore, Artificial Intelligence based motion intention estimation can be subdivided into Electromyography (EMG), Surface Electromyography (SEMG), Extreme Learning Machine (ELM), and Electromyography-based Admittance Control (EAC). Also, Model-based motion intention estimation can be subdivided into Impedance and Admittance control interaction. Having reviewed several papers, EAC and ELM are proposed for efficient motion intention estimation under artificial-based motion intention. In future works, Impedance and Admittance control methods are suggested under model-based motion intention for efficient estimation of motion intention of power assist rehabilitation robot.  In addition, hybrid force/position control and adaptive control are suggested for the selection of control laws. The findings of this review paper can be used for developing an efficient power assist rehabilitation robot with motion intention to aid people with lower or upper limb impairment.

Currently, fully automated rehabilitation robots can assist therapists in providing rehabilitation therapy, hence the patients could get hurt. On the other hand, manual treatment may cause less patient injury but it is tiresome, and there are not enough therapists in most countries. Power assist rehabilitation robots can support the therapists in conducting the treatment and may help to alleviate this problem. The goal of this study is to develop a control strategy for the robot to assist the therapist’s movement in a power assist upper limb rehabilitation treatment. The system combines the advantages of robotic and manual rehabilitation therapy. Torque and position sensors fitted on the power assist upper limb rehabilitation robot arm are used for motion intention estimation. The amount of angular velocity necessary to be delivered to the feedback controller will be determined by predicting the therapist‘s motion intention using the impedance control method. The resulting velocity from the motion intention estimator is incorporated into the Sliding Mode Control - Function Approximation Technique (SMC-FAT) based adaptive controller. The SMC-FAT based adaptive controller in the feedback loop, overcomes the uncertain parameters in the combination of the robot and the human arm. The motion intention estimator forecasts the movement of therapists. The proposed controller is used to regulate elbow flexion and extension motion on a power assist upper limb rehabilitation robot with one degree of freedom (DOF). The proposed control system has been tested using MATLAB simulation and hardware experimental tests. The outcomes demonstrate the effectiveness of the proposed controller in directing the rehabilitation robot to follow the desired trajectory based on the therapist's motion intention, with maximum errors of 0.002rad/sec, 0.005rad/sec and 0.02rad/sec for sinusoidal, constant torque values, and hardware experiment respectively. ABSTRAK: Pada masa ini, robot rehabilitasi automatik sepenuhnya dapat membantu ahli terapi dalam menyediakan terapi pemulihan, tetapi pesakit berkemungkinan tercedera. Sebaliknya, rawatan manual berkemungkinan mengurangkan kecederaan pesakit tetapi ia memenatkan, dan terdapat kurang ahli terapi yang mencukupi di kebanyakan negara. Robot pembantu rehabilitasi dapat membantu ahli terapi dalam menjalankan pemulihan dan mengurangkan masalah ini. Sistem ini menggabungkan kelebihan terapi pemulihan robotik dan manual. Alat pengesan tork dan kedudukan diletakkan pada anggota atas lengan robot rahabilitasi yang digunakan bagi mengesan anggaran jarak pergerakan ahli terapi. Anggaran halaju sudut diperlukan bagi kawalan gerak balas dan dapat diketahui melalui anggaran niat gerakan ahli terapi menggunakan kaedah kawalan impedans. Halaju yang terhasil daripada anggaran niat gerakan diadaptasi ke dalam pengawal adaptif berasaskan Kawalan Mod Gelongsor - Teknik Anggaran Fungsi (SMC-FAT). Pengawal penyesuaian berasaskan SMC-FAT dalam gelung maklum balas, mengatasi parameter yang tidak pasti dalam gabungan robot dan lengan manusia. Penganggar niat gerakan meramalkan gerakan ahli terapi. Pengawal yang dicadangkan digunakan bagi mengawal lenturan siku dan gerakan lanjutan pada robot rehabilitasi dengan satu darjah kebebasan (DOF). Sistem kawalan yang dicadangkan telah diuji menggunakan simulasi MATLAB dan ujian eksperimen perkakasan. Dapatan kajian menunjukkan keberkesanan pengawal yang dicadangkan dalam mengarahkan robot rehabilitasi mengikut trajektori yang dikehendaki berdasarkan niat gerakan ahli terapi, dengan ralat maksimum masing-masing 0.002rad/s dan 0.005rad/s bagi sinusoidal, nilai tork malar, dan eksperimen perkakasan masing-masing.