Vision‐based game design and assessment for physical exercise in a robot‐assisted rehabilitation system

Erdogan, Huseyin; Palaska, Yunus; Maşazade, Engin; Barkana, Duygun Erol; Ekenel, Hazım Kemal

doi:10.1049/iet-cvi.2017.0122

Cited by 8 publications

(8 citation statements)

References 51 publications

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“…Compared to the electromyography (EMG) or vision based rehabilitation system [18,19], the proposed system not only can be expected to fully explore the movement intention of subjects, but also can stretch the upper limb to perform recovery exercises smoothly with multiple trajectories recommended by therapists. Moreover, we can also combine the EMG or vision into our EEG-based system and carry out more research on the EEG processing for the purpose of predicting the recovery of the upper limb.…”

Section: Resultsmentioning

confidence: 99%

EEG-modulated robotic rehabilitation system for upper extremity

Song

Zhao³

et al. 2018

Biotechnology & Biotechnological Equipment

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This paper presents a novel electroencephalogram (EEG)-triggered upper extremity training system. Motor imagery EEG of upper extremity movements is adopted to trigger the Barrett WAM to perform rehabilitation training for patients with stroke. We focus on fully exploring the patient's movement intention and attention from movement imagination EEG and controlling the WAM robot to perform training effectively. A position controller based on fuzzy logic is presented for the rehabilitation system to drive the WAM robot smoothly. Experimental results with seven participants are reported to show the feasibility and effectiveness of the robotic therapy system.

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Section: Resultsmentioning

confidence: 99%

EEG-modulated robotic rehabilitation system for upper extremity

Song

Zhao³

et al. 2018

Biotechnology & Biotechnological Equipment

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“…In this study, we have used the first seven levels, i.e. Level 1 (L1) to Level 7 (L7) in an increased difficulty manner where the game parameters at each level, such as SWT, RS, BS, FN, FS, RN, WWT, were presented in our previous work [27].…”

Section: Methodsmentioning

confidence: 99%

“…In this study, we have used the computer‐based fruit picker game [26], which was first integrated to RehabRoby in our earlier work [27], where different task difficulty levels were defined to reflect different engagement levels to the subjects. Rather than separately analysing the engagement on each difficulty level, in this study, we have dynamically adapted the task difficulty level during the game based on the participant's score.…”

Section: Introductionmentioning

confidence: 99%

“…POSM requires no off‐line training, can be easily ported to any task, and guarantees the maximum number of mistakes that it can make before learning the suitable setting. Similar to our previous work [27], we have used biofeedback sensors, skin conductance (SC), skin temperature (ST), and blood volume pulse (BVP) to record the physiological signals during the execution of the tasks in which the dynamical difficulty level adjustments are made using IDOL or POSM. Such physiological signals are used to evaluate the engagement level of the subjects.…”

Section: Introductionmentioning

confidence: 99%

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Exploring dynamic difficulty adjustment mechanism for rehabilitation tasks using physiological measures and subjective ratings

Ozkul

Palaska

Maşazade

et al. 2019

IET signal process.

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Task engagement is a key factor in sustaining patients’ participation in rehabilitation. Adaptive task difficulty level adjustment techniques are designed to determine the appropriate exercise difficulty level in where subjects are appropriately challenged and engaged without causing any distress. Such adaptive difficulty adjustment within rehabilitation tasks has the potential to individualise training. In this study, the authors have compared two dynamic difficulty level adjustment algorithms, partially ordered set master (POSM) and increment/decrement one level (IDOL), those change the difficulty levels for each individual adaptively based on his/her performance. These two algorithms are integrated into the robot‐assisted rehabilitation system, RehabRoby, and their functionality is explored via a small user study with 20 healthy subjects. The subjects were asked to perform a computer‐based fruit picker game using RehabRoby under these two algorithms. The impacts of the adaptation algorithms are evaluated in terms of engagement of the subjects by looking at their physiological signals, performance (score), and survey results. Experiments show that although POSM on the average suggests easier difficulty levels to the subjects than IDOL, the subjects experience a wider range of difficulty levels in POSM that may help them to become more engaged in the game, which can also be observed by lower skin temperatures of the subjects.

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“…The researchers wanted to capture the participants' emotions during the game, during different phases of the game, and how these emotions could persist over time (van dei Kooij et al, 2019) to represent a real stimulus to continue physiotherapy at home. In one case (Erdogan et al, 2018), a more refined analysis was added to investigate the modes of interaction with the device or application that could increase the engagement score.…”

Section: The Game Experiencementioning

confidence: 99%

Gamification in Rehabilitation: The Role of Subjective Experience in a Multisensory Learning Context – A Narrative Review

Sgubin,

Deodato,

Murena

2023

Gestalt Theory

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Game-based approaches are emerging in many fields, such as education, social sciences, marketing and government. Most studies debate its role in consolidating learning, guided by both internal and external rewards. These approaches are also being applied in rehabilitation, where patients must undergo a re-learning process of motor gestures after an injury to a body structure. In physiotherapy, much importance is given to analytical-functional movement aspects, but less to the recovery of the complete experience, including motivation, perception, and emotional experience of the patient during the process. The aim of this narrative review is to investigate the role of subjective experience in the application of gamification in physiotherapy, considering the added value it provides to recovery by involving neural structures, not just motor functions. By analyzing the most investigated aspects in using gamification in rehabilitation, we will outline the primary methods of investigation into the engagement and emotions involved in the process. Through a selection of scientific articles found on main databases, we identified articles investigating the patient’s experience. The analysis of these articles was based on aspects related to the recovery of movement, the technology used, as well as the methods of investigation and collection of qualitative data regarding the emotions and perceptions of patients during the gamification experience. The results are divided into two primary topics. Overall, this review supports the idea that gamification could represent a rehabilitation approach integrating physiotherapy, more suitable for the final stages of recovery, such as returning to work or sports.

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Vision‐based game design and assessment for physical exercise in a robot‐assisted rehabilitation system

Cited by 8 publications

References 51 publications

EEG-modulated robotic rehabilitation system for upper extremity

EEG-modulated robotic rehabilitation system for upper extremity

Exploring dynamic difficulty adjustment mechanism for rehabilitation tasks using physiological measures and subjective ratings

Gamification in Rehabilitation: The Role of Subjective Experience in a Multisensory Learning Context – A Narrative Review

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