Exploring Innovative Strategies to Enhance Eye-Hand Coordination and Cognitive Functions Through Drone Catching Exercise.

Eye-hand coordination (EHC) is a critical cognitive-motor function that enables individuals to interact effectively with their environment through visually guided hand movements. It plays an essential role in daily activities such as reaching, grasping, and object manipulation. Previous studies have shown that targeted physical activities and sports can enhance EHC performance. However, aging is commonly associated with declines in EHC, executive function, and postural control, which can negatively affect independence in daily living. These age-related changes are also closely linked to cognitive decline and may contribute to the development of mild cognitive impairment (MCI), dementia, and Alzheimer's disease, thereby increasing the burden on families and healthcare systems.

To mitigate these effects, various cognitive-motor and technology-assisted training approaches have been proposed to improve EHC and cognitive function in older adults. While many existing EHC training systems are computerized and implemented using virtual reality (VR) or mixed reality (MR), accumulating evidence suggests that virtual environments may not fully replicate real-world eye-hand interactions. Limitations in depth perception, haptic feedback, and realism may alter visual fixation strategies, movement execution, and overall task performance, potentially reducing training effectiveness compared with real-world interactions.

Given these limitations, it remains unclear whether real-world EHC training provides greater benefits to executive functions and motor performance than virtual training. Therefore, this study aims to compare the acute effects of EHC exercise performed in a real-world environment and a mixed reality passthrough environment among older adults. The proposed EHC training task involves catching a real three-dimensional (3D) object guided by a physical mini drone, inspired by natural human behaviors such as swatting at flying insects, and its virtual counterpart involving a virtual 3D object and drone. The primary objective is to examine differences in executive functions, task performance, and postural stability between real and virtual EHC conditions. By identifying which training modality better supports cognitive-motor performance, this study seeks to inform the design of effective and engaging interventions for healthy aging and early prevention of cognitive decline.

Study Overview

• Status: Completed
• Conditions: Healthy Adult
• Intervention / Treatment: Other: Real Object-Based Catching System; Other: Virtual Object-Based Catching System

• Study Type: Interventional
• Enrollment (Actual): 38
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Taiwan
  • Tainan, Taiwan, 701
    • Motion Analysis Laboratory, Dept. of Biomedical Engineeing, National Cheng Kung University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

• 60 years and older (65 years and older preferred).
• Able to perform regular exercise.
• Normal vision or normal vision after correction.

Exclusion Criteria:

• Have a history of significant chronic diseases such as neurological (e.g., stroke, dementia, Parkinson's disease, poor vision, and hearing loss), cardiovascular, metabolic, pulmonary, or musculoskeletal diseases.
• Have a history of significant motion sickness, active nausea, and vomiting, or epilepsy.
• Fear of wearing a VR headset.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Other
• Allocation: Randomized
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Underwent the virtual system after the real system	Other: Real Object-Based Catching System; This condition involves a participant grasping a physical 3D object located beneath the drone in a real-world environment.; Other: Virtual Object-Based Catching System; The condition involves a participant grasping a virtual counterpart of a physical 3D object within a mixed reality (MR) passthrough environment.
Experimental: Underwent the real system after the virtual system	Other: Real Object-Based Catching System; This condition involves a participant grasping a physical 3D object located beneath the drone in a real-world environment.; Other: Virtual Object-Based Catching System; The condition involves a participant grasping a virtual counterpart of a physical 3D object within a mixed reality (MR) passthrough environment.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Executive Functions via Flanker-ERP Measurement	Each participant underwent Flanker-ERP assessment at three stages: at baseline (pre-intervention) and following both the physical and virtual object-based EHC training sessions.	2 hours
Success Rate (SR)	SR was measured for each participant during object-catching trials across two EHC training modalities: the physical and the virtual 3D object-based drone-catching systems.	1-1.5 hours
Reaction Time (RT)	RT was measured for each participant during object-catching trials across two EHC training modalities: the physical and the virtual 3D object-based drone-catching systems.	1-1.5 hours
Movement Time (MT)	MT was measured for each participant during object-catching trials across two EHC training modalities: the physical and the virtual 3D object-based drone-catching systems.	1-1.5 hours
Peak Hand Velocity (PHV)	PHV was measured for each participant during object-catching trials across two EHC training modalities: the physical and the virtual 3D object-based drone-catching systems.	1-1.5 hours
Time-to-Peak Hand Velocity (TPHV)	TPHV was measured for each participant during object-catching trials across two EHC training modalities: the physical and the virtual 3D object-based drone-catching systems.	1-1.5 hours
Center of Mass (CoM)	The CoM of every participant while performing EHC training tasks was investigated regarding two different EHC training modalities, including physical object-based and virtual object-based drone-catching systems.	1-1.5 hours
Center of Pressure (CoP)	The CoP of every participant while performing EHC training tasks was investigated regarding two different EHC training modalities, including physical object-based and virtual object-based drone-catching systems.	1-1.5 hours

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Subjective participant feedback on perceived task difficulty	Subjective participant feedback on perceived task difficulty regarding the physical and virtual object-based EHC training systems was collected using a 5-point Likert scale (1=very easy, 2=easy, 3=neutral, 4=difficult, and 5=very difficult).	10-15 minutes
Subjective participant feedback on system preference	Subjective participant feedback regarding preference between the physical and virtual object-based EHC training systems was collected.	10-15 minutes
Virtual Reality Sickness Questionnaire (VRSQ)	Adverse effects of the mixed reality environment were evaluated at the end of the experiment using the Virtual Reality Sickness Questionnaire (VRSQ). The VRSQ assesses the severity of nine distinct symptoms on a 4-point scale (none, slight, moderate, and severe). These symptoms include general discomfort, fatigue, headache, eye strain, difficulty focusing, fullness of the head, blurred vision, dizziness with eyes closed, and vertigo.	10-15 minutes

Collaborators and Investigators

• Sponsor: National Cheng-Kung University Hospital

Publications and helpful links

General Publications

• Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004 Mar 15;134(1):9-21. doi: 10.1016/j.jneumeth.2003.10.009.
• Birckhead B, Khalil C, Liu X, Conovitz S, Rizzo A, Danovitch I, Bullock K, Spiegel B. Recommendations for Methodology of Virtual Reality Clinical Trials in Health Care by an International Working Group: Iterative Study. JMIR Ment Health. 2019 Jan 31;6(1):e11973. doi: 10.2196/11973.
• R. B. Davis, S. Õunpuu, D. Tyburski, and J. R. Gage, "A gait analysis data collection and reduction technique," Hum Mov Sci, vol. 10, no. 5, pp. 575-587, 1991, doi: https://doi.org/10.1016/0167-9457(91)90046-Z.
• Lopez-Calderon J, Luck SJ. ERPLAB: an open-source toolbox for the analysis of event-related potentials. Front Hum Neurosci. 2014 Apr 14;8:213. doi: 10.3389/fnhum.2014.00213. eCollection 2014.
• M. Aly and H. Kojima, "Acute moderate-intensity exercise generally enhances neural resources related to perceptual and cognitive processes: A randomized controlled ERP study," Ment Health Phys Act, vol. 19, p. 100363, 2020, doi: https://doi.org/10.1016/j.mhpa.2020.100363.
• Pei YC, Chou SW, Lin PS, Lin YC, Hsu TH, Wong AM. Eye-hand coordination of elderly people who practice Tai Chi Chuan. J Formos Med Assoc. 2008 Feb;107(2):103-10. doi: 10.1016/S0929-6646(08)60123-0.
• B. A. Eriksen and C. W. Eriksen, "Effects of noise letters upon the identification of a target letter in a nonsearch task," Percept Psychophys, vol. 16, no. 1, pp. 143-149, 1974, doi: 10.3758/BF03203267.
• Lavoie E, Hebert JS, Chapman CS. Comparing eye-hand coordination between controller-mediated virtual reality, and a real-world object interaction task. J Vis. 2024 Feb 1;24(2):9. doi: 10.1167/jov.24.2.9.
• A. Dalia Blaga, M. Frutos-Pascual, C. Creed, and I. Williams, "A Grasp on Reality: Understanding Grasping Patterns for Object Interaction in Real and Virtual Environments," in 2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), 2021, pp. 391-396. doi: 10.1109/ISMAR-Adjunct54149.2021.00090.
• Chan PT, Chang WC, Chiu HL, Kao CC, Liu D, Chu H, Chou KR. Effect of interactive cognitive-motor training on eye-hand coordination and cognitive function in older adults. BMC Geriatr. 2019 Jan 28;19(1):27. doi: 10.1186/s12877-019-1029-y.
• "Dementia Prevention and Care Policy and Action Plan 2.0 Ministry of Health and Welfare," 2018.
• P. Lenik, K. Przednowek, M. Śliż, G. Bobula, and J. Lenik, "The impact of exercises with a reaction ball on the eye-hand coordination of basketball players," Apr. 2017.
• C. A. Manning and J. K. Ducharme, "Chapter 6 - Dementia Syndromes in the Older Adult," in Handbook of Assessment in Clinical Gerontology (Second Edition), Second Edition., P. A. Lichtenberg, Ed., San Diego: Academic Press, 2010, pp. 155-178. doi: https://doi.org/10.1016/B978-0-12-374961-1.10006-5.
• Heintz Walters B, Huddleston WE, O'Connor K, Wang J, Hoeger Bement M, Keenan KG. The role of eye movements, attention, and hand movements on age-related differences in pegboard tests. J Neurophysiol. 2021 Nov 1;126(5):1710-1722. doi: 10.1152/jn.00629.2020. Epub 2021 Oct 13.
• Van Halewyck F, Lavrysen A, Levin O, Boisgontier MP, Elliott D, Helsen WF. Both age and physical activity level impact on eye-hand coordination. Hum Mov Sci. 2014 Aug;36:80-96. doi: 10.1016/j.humov.2014.05.005. Epub 2014 Jun 22.
• Rand MK, Stelmach GE. Effects of hand termination and accuracy requirements on eye-hand coordination in older adults. Behav Brain Res. 2011 May 16;219(1):39-46. doi: 10.1016/j.bbr.2010.12.008. Epub 2010 Dec 14.
• Niechwiej-Szwedo E, Wu S, Nouredanesh M, Tung J, Christian LW. Development of eye-hand coordination in typically developing children and adolescents assessed using a reach-to-grasp sequencing task. Hum Mov Sci. 2021 Dec;80:102868. doi: 10.1016/j.humov.2021.102868. Epub 2021 Sep 9.

Study record dates

Study Major Dates

• Study Start (Actual): October 19, 2024
• Primary Completion (Actual): December 23, 2024
• Study Completion (Actual): December 23, 2024

Study Registration Dates

• First Submitted: January 22, 2026
• First Submitted That Met QC Criteria: February 1, 2026
• First Posted (Actual): February 6, 2026

Study Record Updates

• Last Update Posted (Actual): February 6, 2026
• Last Update Submitted That Met QC Criteria: February 1, 2026
• Last Verified: February 1, 2026

More Information

Terms related to this study

• Keywords: Executive Functions; Gerontechnology; Behavioral Responses; Eye-Hand Coordination (EHC); Physical and Virtual Objects; Mixed Reality (MR); Postural Stabilities
• Other Study ID Numbers: 113-420

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED
• IPD Plan Description: The study is currently under manuscript preparation.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No