Upper extremity rehabilitation is critical for individuals with neurological disorders such as Parkinson's disease, where motor impairments significantly affect daily functionality.
Objective
This study presents the design and prototyping of a novel rehabilitation glove aimed at improving hand and wrist motor recovery through gamified therapy.
Methods
The proposed glove features wireless communication via a Wi-Fi network, adaptability to various hand sizes, and an integrated strength training mechanism using resistance bands and metal hooks. The glove consists of a lightweight forearm frame, a palm component, and a textile glove with embedded mechanical connectors, all designed based on anthropometric data. 3D printing techniques were employed using PLA and flexible TPU materials to create a modular, low-cost, and comfortable structure. The system interfaces with an interactive game, allowing users to control an avatar (bee) through hand movements, promoting motivation and active engagement.
Results
The prototype effectively addresses key limitations of previous rehabilitation systems, including mobility, comfort, adaptability, and functionality.
Conclusion
The proposed rehabilitation glove offers a promising solution for home-based neurorehabilitation.
FrazzittaGMaestriRBertottiG, et al.Intensive rehabilitation treatment in early Parkinson’s disease: a randomized pilot study with a 2-year follow-up. Neurorehabil Neural Repair2015; 29: 123–131.
2.
FanMCLiSFSunP, et al. Early intensive rehabilitation for patients with traumatic brain injury: a prospective pilot trial. World Neurosurg2020; 137: e183–e188.
3.
LaverKELangeBGeorgeS, et al.Virtual reality for stroke rehabilitation. Cochrane Database Syst Rev2017; 11: CD008349.
4.
IosaMAydinMCandeliseC, et al.The michelangelo effect: art improves the performance in a virtual reality task developed for upper limb neurorehabilitation. Front Psychol2021; 11: 611956.
5.
RojoASantos-PazJÁSánchez-PicotÁ, et al.Farmday: a gamified virtual reality neurorehabilitation application for upper limb based on activities of daily living. Appl Sci2022; 12: 7068.
6.
LülsdorffKJunkerFBStuderB, et al.Neurorehabilitation of the upper extremity – immersive virtual reality vs. Electromechanically assisted training. A comparative study. Front Neurol2023; 14: 1290637.
7.
AliASKumaranDSUnniA, et al.Effectiveness of an intensive, functional, and gamified rehabilitation program on upper limb function in people with stroke (EnteRtain): a multicenter randomized clinical trial. Neurorehabil Neural Repair2024; 38: 243–256.
8.
PillaiASunnyMSHShahriaMT, et al.Gamification of upper limb rehabilitation in mixed-reality environment. Appl Sci2022; 12: 12260.
9.
LevinMFDemersM. Motor learning in neurological rehabilitation. Disabil Rehabil2021; 43: 3445–3453.
10.
HuangCYChiangWCYehYC, et al.Effects of virtual reality-based motor control training on inflammation, oxidative stress, neuroplasticity and upper limb motor function in patients with chronic stroke: a randomized controlled trial. BMC Neurol2022; 22: 1–14.
11.
VaezipourAAldridgeDKoenigS, et al.‘It’s really exciting to think where it could go’: a mixed-method investigation of clinician acceptance, barriers and enablers of virtual reality technology in communication rehabilitation. Disabil Rehabil2022; 44: 3946–3958.
12.
GhermanBZimaIVaidaC, et al.Robotic systems for hand rehabilitation—past, present and future. Technologies2025; 13: 37.
13.
WeerasooryWAKCKumasaruURENipunHMW, et al. A robotic hand for rehabilitation of wrist and fingers. In: 2023 8th International Conference on Information Technology Research (ICITR). IEEE, pp. 1–6.
14.
FerreiraBMenezesP. Gamifying motor rehabilitation therapies: challenges and opportunities of immersive technologies. Information2020; 11: 88.
15.
JacksonGAbdullahHA. Development and testing of a soft exoskeleton robotic hand training device. Sensors2023; 23: 8395.
16.
MendesLCMarquesIAAlvesCM, et al.Multidimensional assessment of individuals with Parkinson’s disease: development and structure validation of a self-assessment questionnaire. Healthcare2022; 10: 1823.
17.
AitkenCSSamotusONaiduAS, et al.Force control issues in upper and lower limbs in Parkinson’s disease and freezing of gait. IEEE Trans Neural Syst Rehabil Eng2024; 32: 577–586.
18.
CohenNKizonyR. Patient perspectives on upper-limb daily function in Parkinson’s disease. Mov Disord Clin Pract2025; 12: 196–202.
19.
PoeweWSeppiKTannerCM, et al.Parkinson disease. Nat Rev Dis Primers2017; 3: 1–21.
20.
Feingold-PolakRBarzelOLevy-TzedekS. A robot goes to rehab: a novel gamified system for long-term stroke rehabilitation using a socially assistive robot—methodology and usability testing. J Neuroeng Rehabil2021; 18: 1–18.
21.
WhiteMVining RadomskiMFinkelsteinM, et al.Assistive/socially assistive robotic platform for therapy and recovery: patient perspectives. Int J Telemed Appl2013; 2013: 948087.
22.
ProulxCEBeaulacMDavidM, et al.Review of the effects of soft robotic gloves for activity-based rehabilitation in individuals with reduced hand function and manual dexterity following a neurological event. J Rehabil Assist Technol Eng2020; 7: 205566832091813. https://doi.org/101177/2055668320918130
23.
XieQMengQYuW, et al.Design of a SMA-based soft composite structure for wearable rehabilitation gloves. Front Neurorobot2023; 17: 1047493.
24.
OziokoODahiyaR. Smart tactile gloves for haptic interaction, communication, and rehabilitation. Adv Intell Syst2022; 4: 2100091.
25.
ChenXGongLWeiL, et al.A wearable hand rehabilitation system with soft gloves. IEEE Trans Industr Inform2021; 17: 943–952.
26.
WangQKangBKristenssonPO. Supporting Playful Rehabilitation in the Home using Virtual Reality Headsets and Force Feedback Gloves. Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2022 2022; 504–513.
MendesLCCabralAMAlvesCM, et al. Objective Evaluation of Bradykinesia Using the RehaBEElitation Serious Game. 2022 IEEE Workshop on Complexity in Engineering, COMPENG 2022. Epub ahead of print 2022. DOI: 10.1109/COMPENG50184.2022.9905437.
MendesLCde SáAARMarquesIA, et al.RehaBEElitation: the architecture and organization of a serious game to evaluate motor signs in Parkinson’s disease. PeerJ Comput Sci2023; 9: 1–30.
35.
RudolfsDRenatoCMauriceB. Body segment parameters: a survey of measurement techniques. O&P virtual library. Artif Limbs1964; 8: 44–66.
36.
SayadizadehMDaliriMRahimiM, et al.Grip and pinch strength prediction models based on hand anthropometric parameters: an analytic cross-sectional study. BMC Musculoskelet Disord2024; 25: 809.
37.
HsuehMHLaiCJWangSH, et al.Effect of printing parameters on the thermal and mechanical properties of 3D-printed PLA and PETG, using fused deposition modeling. Polymers (Basel)2021; 13: 1758.
38.
Ramírez-RevillaSCamacho-ValenciaDGonzales-CondoriEG, et al.Evaluation and comparison of the degradability and compressive and tensile properties of 3D printing polymeric materials: PLA, PETG, PC, and ASA. MRS Commun2023; 13: 55–62.
KilaniHAbu-EishehA. Optimum anthropometric criteria for ideal body composition related fitness. Sultan Qaboos Univ Med J2010; 10: 74.
42.
János BretzKJobbágyÁBretzK. Force measurement of hand and fingers. Biomechanica Hungarica III évfolyam.
43.
EschweilerJPrasterMQuackV, et al.Musculoskeletal modeling of the wrist via a multi body simulation. Life2022; 12: 581.
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