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Assessment of robotic patient simulators for training in manual physical therapy examination techniques.

Ishikawa S, Okamoto S, Isogai K, Akiyama Y, Yanagihara N, Yamada Y - PLoS ONE (2015)

Bottom Line: Experienced clinicians were requested to rate the simulated symptoms in terms of severity, and the consistency of their ratings was used as a performance measure.In this case, the clinicians were requested to classify the simulated resistances in terms of symptom type, and the average ratios of their answers were used as performance measures.The assessment measures have potential for use in comparing several patient simulators for training physical therapists, rather than as absolute indices for developing a standard.

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical Science and Engineering, Graduate School of Engineering, Nagoya University, Nagoya, Japan.

ABSTRACT
Robots that simulate patients suffering from joint resistance caused by biomechanical and neural impairments are used to aid the training of physical therapists in manual examination techniques. However, there are few methods for assessing such robots. This article proposes two types of assessment measures based on typical judgments of clinicians. One of the measures involves the evaluation of how well the simulator presents different severities of a specified disease. Experienced clinicians were requested to rate the simulated symptoms in terms of severity, and the consistency of their ratings was used as a performance measure. The other measure involves the evaluation of how well the simulator presents different types of symptoms. In this case, the clinicians were requested to classify the simulated resistances in terms of symptom type, and the average ratios of their answers were used as performance measures. For both types of assessment measures, a higher index implied higher agreement among the experienced clinicians that subjectively assessed the symptoms based on typical symptom features. We applied these two assessment methods to a patient knee robot and achieved positive appraisals. The assessment measures have potential for use in comparing several patient simulators for training physical therapists, rather than as absolute indices for developing a standard.

No MeSH data available.


Related in: MedlinePlus

Training in manual examination techniques using a wearable knee joint.Left: Healthy person wearing the robotic knee joint. Right: Trainee therapist
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pone.0126392.g001: Training in manual examination techniques using a wearable knee joint.Left: Healthy person wearing the robotic knee joint. Right: Trainee therapist

Mentions: As shown in Fig 1, we used a wearable robot [8, 9] to simulate the resistance forces of the knee of a patient. In physical therapy educational facilities, trainees commonly study manual examination techniques in pairs. One trainee acts as a patient and the other practices the techniques. The wearable patient robot is particularly designed for such training situations. The trainee that plays the part of a patient wears the robot and relaxes to avoid generating voluntary forces. The other trainee manually tests the dummy patient leg for which the robot produces a resistance force to simulate joint impairments such as abnormal muscular tensions or neural disorders.


Assessment of robotic patient simulators for training in manual physical therapy examination techniques.

Ishikawa S, Okamoto S, Isogai K, Akiyama Y, Yanagihara N, Yamada Y - PLoS ONE (2015)

Training in manual examination techniques using a wearable knee joint.Left: Healthy person wearing the robotic knee joint. Right: Trainee therapist
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4414623&req=5

pone.0126392.g001: Training in manual examination techniques using a wearable knee joint.Left: Healthy person wearing the robotic knee joint. Right: Trainee therapist
Mentions: As shown in Fig 1, we used a wearable robot [8, 9] to simulate the resistance forces of the knee of a patient. In physical therapy educational facilities, trainees commonly study manual examination techniques in pairs. One trainee acts as a patient and the other practices the techniques. The wearable patient robot is particularly designed for such training situations. The trainee that plays the part of a patient wears the robot and relaxes to avoid generating voluntary forces. The other trainee manually tests the dummy patient leg for which the robot produces a resistance force to simulate joint impairments such as abnormal muscular tensions or neural disorders.

Bottom Line: Experienced clinicians were requested to rate the simulated symptoms in terms of severity, and the consistency of their ratings was used as a performance measure.In this case, the clinicians were requested to classify the simulated resistances in terms of symptom type, and the average ratios of their answers were used as performance measures.The assessment measures have potential for use in comparing several patient simulators for training physical therapists, rather than as absolute indices for developing a standard.

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical Science and Engineering, Graduate School of Engineering, Nagoya University, Nagoya, Japan.

ABSTRACT
Robots that simulate patients suffering from joint resistance caused by biomechanical and neural impairments are used to aid the training of physical therapists in manual examination techniques. However, there are few methods for assessing such robots. This article proposes two types of assessment measures based on typical judgments of clinicians. One of the measures involves the evaluation of how well the simulator presents different severities of a specified disease. Experienced clinicians were requested to rate the simulated symptoms in terms of severity, and the consistency of their ratings was used as a performance measure. The other measure involves the evaluation of how well the simulator presents different types of symptoms. In this case, the clinicians were requested to classify the simulated resistances in terms of symptom type, and the average ratios of their answers were used as performance measures. For both types of assessment measures, a higher index implied higher agreement among the experienced clinicians that subjectively assessed the symptoms based on typical symptom features. We applied these two assessment methods to a patient knee robot and achieved positive appraisals. The assessment measures have potential for use in comparing several patient simulators for training physical therapists, rather than as absolute indices for developing a standard.

No MeSH data available.


Related in: MedlinePlus