Approaches for increasing patient’s engagement and motivation in exer-games-based autonomous telerehabilitation

Pezzera, Manuel; Tironi, Alessandro; Essenziale, Jacopo; Mainetti, Renato; Borghese, N. Alberto

doi:10.1109/segah.2019.8882468

Cited by 7 publications

(6 citation statements)

References 22 publications

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“…This can, on one side, support gamification to boost child motivation in performing the exercises (e.g., virtual medals, points, leader boards, ranking, and so forth, cf. [63]), while on the other side it connects all the stakeholders and provides them a place where to exchange information, data, suggestions, and where they can find continuous support from peers.…”

Section: Discussionmentioning

confidence: 99%

Hand Rehabilitation and Telemonitoring through Smart Toys

Borghese

Essenziale

Mainetti

et al. 2019

Sensors

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We describe here a platform for autonomous hand rehabilitation and telemonitoring of young patients. A toy embedding the electronics required to sense fingers pressure in different grasping modalities is the core element of this platform. The system has been realized following the user-centered design methodology taking into account stakeholder needs from start: clinicians require reliable measurements and the ability to get a picture remotely on rehabilitation progression; children have asked to interact with a pleasant and comfortable object that is easy to use, safe, and rewarding. These requirements are not antithetic, and considering both since the design phase has allowed the realization of a platform reliable to clinicians and keen to be used by young children.

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Section: Discussionmentioning

confidence: 99%

Hand Rehabilitation and Telemonitoring through Smart Toys

Borghese

Essenziale

Mainetti

et al. 2019

Sensors

Self Cite

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“…Finally, acknowledging that physical therapy can be physically and mentally taxing, we enabled a Home page menu such that patients could press a button to pause the software and rest. This feature is particularly important for telerehabilitation of stroke, as many patients may feel pain or fatigue, discouraging them from engaging in the exercise [ 14 ].…”

Section: Methodsmentioning

confidence: 99%

“…Despite the promising prospects, the advantages of telerehabilitation are often not realized, as patients fail to adhere to their prescribed regimen in the absence of a physical therapist [ 13 , 14 ]. One of the primary factors pinpointing a lack of adherence is a lack of motivation [ 13 , 14 ]. To address this critical limitation, innumerable efforts were invested in gamification of telerehabilitation [ 15 - 17 ].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Classification of Human Movements in Virtual Reality–Based Serious Games: Preclinical Rehabilitation Study in Citizen Science

Ventura¹,

Hughes²,

Nov³

et al. 2022

JMIR Serious Games

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Background Sustained engagement is essential for the success of telerehabilitation programs. However, patients’ lack of motivation and adherence could undermine these goals. To overcome this challenge, physical exercises have often been gamified. Building on the advantages of serious games, we propose a citizen science–based approach in which patients perform scientific tasks by using interactive interfaces and help advance scientific causes of their choice. This approach capitalizes on human intellect and benevolence while promoting learning. To further enhance engagement, we propose performing citizen science activities in immersive media, such as virtual reality (VR). Objective This study aims to present a novel methodology to facilitate the remote identification and classification of human movements for the automatic assessment of motor performance in telerehabilitation. The data-driven approach is presented in the context of a citizen science software dedicated to bimanual training in VR. Specifically, users interact with the interface and make contributions to an environmental citizen science project while moving both arms in concert. Methods In all, 9 healthy individuals interacted with the citizen science software by using a commercial VR gaming device. The software included a calibration phase to evaluate the users’ range of motion along the 3 anatomical planes of motion and to adapt the sensitivity of the software’s response to their movements. During calibration, the time series of the users’ movements were recorded by the sensors embedded in the device. We performed principal component analysis to identify salient features of movements and then applied a bagged trees ensemble classifier to classify the movements. Results The classification achieved high performance, reaching 99.9% accuracy. Among the movements, elbow flexion was the most accurately classified movement (99.2%), and horizontal shoulder abduction to the right side of the body was the most misclassified movement (98.8%). Conclusions Coordinated bimanual movements in VR can be classified with high accuracy. Our findings lay the foundation for the development of motion analysis algorithms in VR-mediated telerehabilitation.

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“…Such PCG systems in commercial games mostly do not take player behavior into account [15]. For exergames, Pezzera et al [7] showed that using PCG to create visually different levels without adapting to the player can already reduce repetition and therefore increase player motivation.…”

Section: Related Workmentioning

confidence: 99%

“…For exergames, DDA has the additional benefit of adjusting the difficulty of the exercises such that they provide efficient physical training without overburdening the player [6]. Incorporating PCG into exergames to create visually different levels has been shown to reduce repetition between different play sessions [7].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Difficulty Adjustment in Virtual Reality Exergames through Experience-driven Procedural Content Generation

Huber,

Mertes,

Rangelova

et al. 2021

Preprint

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Virtual Reality (VR) games that feature physical activities have been shown to increase players' motivation to do physical exercise. However, for such exercises to have a positive healthcare effect, they have to be repeated several times a week. To maintain player motivation over longer periods of time, games often employ Dynamic Difficulty Adjustment (DDA) to adapt the game's challenge according to the player's capabilities. For exercise games, this is mostly done by tuning specific in-game parameters like the speed of objects. In this work, we propose to use experience-driven Procedural Content Generation for DDA in VR exercise games by procedurally generating levels that match the player's current capabilities. Not only finetuning specific parameters but creating completely new levels has the potential to decrease repetition over longer time periods and allows for the simultaneous adaptation of the cognitive and physical challenge of the exergame.As a proof-of-concept, we implement an initial prototype in which the player must traverse a maze that includes several exercise rooms, whereby the generation of the maze is realized by a neural network. Passing those exercise rooms requires the player to perform physical activities. To match the player's capabilities, we use Deep Reinforcement Learning to adjust the structure of the maze and to decide which exercise rooms to include in the maze. We evaluate our prototype in an exploratory user study utilizing both biodata and subjective questionnaires.

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Approaches for increasing patient’s engagement and motivation in exer-games-based autonomous telerehabilitation

Cited by 7 publications

References 22 publications

Hand Rehabilitation and Telemonitoring through Smart Toys

Hand Rehabilitation and Telemonitoring through Smart Toys

Data-Driven Classification of Human Movements in Virtual Reality–Based Serious Games: Preclinical Rehabilitation Study in Citizen Science

Dynamic Difficulty Adjustment in Virtual Reality Exergames through Experience-driven Procedural Content Generation

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