Purpose: To clarify the feature values of exercise therapy that can differentiate students and expert therapists and use this information as a reference for exercise therapy education. Methods: The participants were therapists with 5 or more years of clinical experience and 4th year students at occupational therapist training schools who had completed their clinical practices. The exercise therapy task included Samothrace (code name, SAMO) exercises implemented on the elbow joint based on the elbow flexion angle, angular velocity, and exercise interval recordings. For analyses and student/therapist comparisons, the peak flexion angle, peak velocity, and movement time were calculated using data on elbow angle changes acquired via SAMO. Subsequently, bootstrap data were generated to differentiate between the exercise therapy techniques adopted by therapists and students, and a support vector machine was used to generate four types of data combinations with the peak flexion angle, peak velocity, and movement time values. These data were used to estimate and compare the respective accuracies with the Friedman test. Results: The peak flexion angles were significantly smaller in the case of students. Furthermore, the peak velocities were larger, the peak flexion angles were smaller, and the movement times were shorter compared with those of therapists. The combination of peak velocity and peak flexion angle yielded the highest diagnostic accuracies. Conclusion: When students and therapists performed upper limb exercise therapy techniques based on the kinematics movement of a robot arm, the movement speeds and joint angles differed. The combination of peak velocity and peak flexion angle was the most effective classifier used for the differentiation of the abilities of students and therapists. The peak velocity and peak flexion angle of the therapist group can be used as a reference for students when they learn upper limb therapeutic exercise techniques.

Long-term repetitive movements, such as opening and closing scissors, increase strain on muscles and joints. Amplitude probability distribution function (APDF) analysis of surface electromyogram (sEMG) data was used to quantify the burden of muscle activity. We aimed to test the hypothesis that scissors with a power-support device assist repetitive thumb movements to reduce potential myoelectric activity. Twenty female university students who met the eligibility criteria performed a cutting experiment, with and without power-support device scissors. The primary outcome was a change in muscle load due to sEMG data that were analyzed using APDF, and the secondary outcomes investigated the occurrence of muscle fatigue and pain. The adductor pollicis muscle showed a significant decrease in muscle activity with power assistance. In addition, it was also found that fatigue and pain of the thumb and on the radial side of the forearm were significantly lower under the power-assisted conditions. The results of this study suggest that the assistive action of scissors with a power-support device compensate for muscle load on the thenar eminence. This may be used as a reference value to prevent the occurrence of hand disorders for hairdressers.

In this study, the motion therapy elements necessary for student education were clarified through comparison of the therapeutic motion techniques of therapists and students using an educational arm robot (Samothrace: SAMO). Eight therapists and 25 fourth-year students participated. The therapeutic motion therapy task was a reciprocating exercise in which the elbow joint of SAMO was flexed from an extended position and then re-extended. This was performed for three types of muscle tone intensities (mild, moderate, and severe), and the peak velocity, angle ratio, velocity time, and movement time were recorded using SAMO. These data were then compared using analysis of covariance. It was found that the SAMO elbow joint kinematic data generated by therapists differed significantly from those of students for different muscle tones. Multiple comparisons showed that the therapeutic motion techniques of students were associated with a higher peak velocity, smaller peak angle ratio, and shorter peak velocity time and movement time than those of the therapists. Thus, when students learn therapeutic motion techniques, they should be taught to (1) deal with multiple muscle tone intensities and (2) reduce the joint movement speed applied to the patient to extend the exercise time and ensure maximum joint movement range.

We aimed to compare the effects of practical learning using an educational robot arm (Samothrace: SAMO) and a student simulating a patient in students’ acquisition of therapeutic motion techniques. For this study, twenty-five fourth-year students and eight therapists were included. Students were randomly assigned to the robot practice (Robo) group (n = 13) or conventional practice (Human) group (n = 12), wherein students practiced among themselves. The experimental procedure comprised a pre-practice test, practice, and post-practice test. We calculated the peak angle ratio, peak velocity, and movement time during flexion and extension using the elbow joint angle data. Machine learning was performed on students’ pre-practice and used therapists’ data to create identifiers that distinguish students and therapists to determine whether the students’ skills improved to the level of a therapist. However, regarding other kinematics data, there were no main effects or interactions between groups pre- and post-practice. Identifiers were created from machine learning of kinematics data pre-practice, and the effect was evaluated post-practice. A higher number of students in the Robo group were determined to possess skills at a therapist’s level. This study suggests the effectiveness of a simulation robot that can reproduce a patient’s pathological motions in educating students on upper limb exercise therapy.