基于运动表现的双人上肢交互辅助策略设计

doi:10.3969/j.issn.1006-9771.2024.10.014

Abstract

Abstract:

Objective To propose a motion performance-based adaptive assistance strategy to balance the differences in movement intensity caused by varying motor abilities in dual-person upper limb rehabilitation training.

Methods A competitive interactive task suitable for dual-person rehabilitation training was designed. Two kinematic evaluation metrics were introduced, and fuzzy logic was applied to comprehensively assess the patients' motor performance. An adaptive control system framework was constructed to dynamically adjust the robot's assistance level through a difference-based adaptive step-size piecewise function. Four healthy persons were recruited randomly to test the system.

Results The proposed strategy could adaptively adjust the level of assistance based on the participants' motor performance, balancing the differences in motor abilities between them and enabling both parties to engage at a similar competitive level.

Conclusion The motion performance-based adaptive assistance strategy and adaptive control system framework may balance the skill levels between participants with different motor abilities, avoiding the imbalance of training intensity during dual-person interactive rehabilitation.

Key words: upper limb rehabilitation, motion performance, assistance strategy, fuzzy logic, human-robot interaction

CLC Number:

R496

ZHANG Yichen, FEI Sixian, SUN Qing, LI Xuanxuan, GUO Shuai. A motion performance-based assistance strategy for dual-person upper limb interaction[J]. Chinese Journal of Rehabilitation Theory and Practice, 2024, 30(10): 1232-1240.

Figures/Tables 13

Figure 1

Figure 2

Figure 3

Table 1

Fuzzy rules"

序号	$v - n$	$t n$	$T C F n$
1	B	B	L
2	B	M	CL
3	B	G	M
4	M	B	CL
5	M	M	M
6	M	G	CH
7	G	B	M
8	G	M	CH
9	G	G	H

Table 1

Figure 4

Figure 5

Figure 6

Figure 7

Table 2

Parameters of the experiment"

参数	数值	参数	数值
$M$	$d i a g (0.5, 0.5, 0.5)$ kg	$d$	1 mm
$D$	$d i a g (40, 40, 40)$ Ns/m	$Δ T$	8 ms

Table 2

Figure 8

Figure 9

Figure 10

Figure 11

References 34

[1]	MACKAY J, MENSAH G A. The atlas of heart disease and stroke[M]. Geneva: World Health Organization, 2004.
[2]	KIM Y W. Update on stroke rehabilitation in motor impairment[J]. Brain Neurorehabil, 2022, 15(2): e12.
[3]	JACKSON N, HAXTON E, MORRISON K, et al. Reflections on 50 years of neuroscience nursing: the growth of stroke nursing[J]. J Neurosci Nurs, 2018, 50(4): 188-192. doi: 10.1097/JNN.0000000000000375 pmid: 29750679
[4]	TATER P, PANDEY S. Post-stroke movement disorders: clinical spectrum, pathogenesis, and management[J]. Neurol India, 2021, 69(2): 272-283. doi: 10.4103/0028-3886.314574 pmid: 33904435
[5]	STINEAR C M, LANG C E, ZEILER S, et al. Advances and challenges in stroke rehabilitation[J]. Lancet Neurol, 2020, 19(4): 348-360. doi: S1474-4422(19)30415-6 pmid: 32004440
[6]	LIU X F, WANG G H, MIAO F R. The effect of early cognitive training and rehabilitation for patients with cognitive dysfunction in stroke[J]. Int J Method Psychiatr Res, 2021, 30(3): e1882.
[7]	LIANG H, LIU S, WANG Y, et al. Multi-user upper limb rehabilitation training system integrating social interaction[J]. Comput Graph, 2023, 111: 103-110.
[8]	GANESH G, TAKAGI A, OSU R, et al. Two is better than one: Physical interactions improve motor performance in humans[J]. Sci Rep, 2014, 4: 3824. doi: 10.1038/srep03824 pmid: 24452767
[9]	BISHOP L, BROWN S C, GARDENER H, et al. The association between social networks and functional recovery after stroke[J]. [ahead of print]. Int J Stroke, 2024. doi: 10.1177/17474930241283167.
[10]	BATSON J P, KATO Y, SHUSTER K, et al. Haptic coupling in dyads improves motor learning in a simple force field[C]. Montreal, Canada:42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020.
[11]	THIELBAR K O, TRIANDAFILOU K M, BARRY A J, et al. Home-based upper extremity stroke therapy using a multiuser virtual reality environment: a randomized trial[J]. Arch Phys Med Rehabil, 2020, 101(2): 196-203.
[12]	BAUR K, ROHRBACH N, HERMSDÖRFER J, et al. The "Beam-Me-In Strategy": remote haptic therapist-patient interaction with two exoskeletons for stroke therapy[J]. J Neuroeng Rehabil, 2019, 16(1): 85.
[13]	KÜÇÜKTABAK E B, KIM S J, WEN Y, et al. Human-machine-human interaction in motor control and rehabilitation: a review[J]. J Neuroeng Rehabil, 2021, 18(1): 183.
[14]	OZKUL F, PALASKA Y, MASAZADE E, et al. Exploring dynamic difficulty adjustment mechanism for rehabilitation tasks using physiological measures and subjective ratings[J]. IET Sign Proc, 2019, 13(3): 378-386.
[15]	ÖZKUL F, BARKANA D E, MASAZADE E. Dynamic difficulty level adjustment based on score and physiological signal feedback in the robot-assisted rehabilitation system, RehabRoby[J]. IEEE Robot Autom Lett, 2020, 6(2): 447-454.
[16]	HARE R, TANG Y. Player modeling and adaptation methods within adaptive serious games[J]. IEEE Trans Comput Social Syst, 2022, 10(4): 1939-1950.
[17]	苗青, 孙晨阳, 张明明, 等. 基于任务表现的机器人辅助康复自适应控制策略[J]. 机器人, 2021, 43(5): 539-546, 556. doi: 10.13973/j.cnki.robot.200555
	MIAO Q, SUN C Y, ZHANG M M, et al. Performance-based adaptive control strategy for robot-assisted rehabilitation[J]. Robot, 2021, 43(5): 539-546, 556. doi: 10.13973/j.cnki.robot.200555
[18]	CATALÁN J M, GARCÍA-PÉREZ J V, BLANCO A, et al. Differences in physiological reactions due to a competitive rehabilitation game modality[J]. Sensors, 2021, 21(11): 3681.
[19]	NAVARRO M D, LLORENS R, BORREGO A, et al. Competition enhances the effectiveness and motivation of attention rehabilitation after stroke. A randomized controlled trial[J]. Front Hum Neurosci, 2020, 14: 575403.
[20]	TANAKA Y, WADA S. Analysis of cooperative motions in a ball-manipulation task toward robot-aided rehabilitation for the upper extremity[C]. Munich, Germany: IEEE International Conference on Cyborg and Bionic Systems (CBS), 2019.
[21]	蔡华年, 费思先, 张忆晨, 等. 基于导纳控制的双边康复机器人运动辅助分析[J]. 中国康复理论与实践, 2023, 29(9): 1104-1109. doi: 10.3969/j.issn.1006-9771.2023.09.015
	CAI H N, FEI S X, ZHANG Y C, et al. Motion assistance analysis for robot-assisted tele-rehabilitation based on bilateral admittance control[J]. Chin J Rehabil Theory Pract, 2023, 29(9): 1104-1109.
[22]	GORŠIČ M, DARZI A, NOVAK D. Comparison of two difficulty adaptation strategies for competitive arm rehabilitation exercises[C]. QEII Center, London: International Conference on Rehabilitation Robotics (ICORR), 2017.
[23]	ANDRADE K O, PASQUAL T B, CAURIN G A P, et al. Dynamic difficulty adjustment with evolutionary algorithm in games for rehabilitation robotics[C]. Orlando, FL, USA: IEEE International Conference on Serious Games and Applications for Health (SeGAH), 2016.
[24]	MACE M, KINANY N, RINNE P, et al. Balancing the playing field: collaborative gaming for physical training[J]. J Neuroeng Rehabil, 2017, 14(1): 116.
[25]	KLOBUCKA S, ZIAKOVA E, KLOBUCKY R. The effect of virtual reality environment during robotic-assisted locomotor training on gross motor functions in patients with cerebral palsy[J]. Česká Slovenská Neurol Neurosurg, 2013, 76(6): 702-711.
[26]	CAO R, CHENG L, YANG C G, et al. Iterative assist-as-needed control with interaction factor for rehabilitation robots[J]. Sci Chin Technol Sci, 2021, 64(4): 836-846.
[27]	FEI S, SUN Q, ZHANG Y, et al. Performance-based assistance control for upper limb robotic mirror therapy[J]. J Bionic Eng, 2024 21: 2291-2301.
[28]	MOUNIS S Y A, AZLAN N Z, SADO F. Assist-as-needed control strategy for upper-limb rehabilitation based on subject's functional ability[J]. Meas Control, 2019, 52(9/10): 1354-1361.
[29]	LUO L, PENG L, WANG C, et al. A greedy assist-as-needed controller for upper limb rehabilitation[J]. IEEE Trans Neural Network Learn Syst, 2019, 30(11): 3433-3443.
[30]	SHAHBAZI M, ATASHZAR S F, TAVAKOLI M, et al. Robotics-assisted mirror rehabilitation therapy: a therapist-in-the-loop assist-as-needed architecture[J]. IEEE/ASME Trans Mechatron, 2016, 21(4): 1954-1965.
[31]	MOUNIS S Y A, AZLAN N Z, SADO F. Assist-as-needed robotic rehabilitation strategy based on Z-spline estimated functional ability[J]. IEEE Access, 2020, 8: 157557-157571.
[32]	QIAN C, LI W, JIA T, et al. Quantitative assessment of motor function by an end-effector upper limb rehabilitation robot based on admittance control[J]. Appl Sci, 2021, 11(15): 6854.
[33]	LIU Y, SONG Q, LI C, et al. Quantitative assessment of motor function for patients with a stroke by an end-effector upper limb rehabilitation robot[J]. BioMed Res Int, 2020, 2020: 5425741.
[34]	GHANAVATI M A, VAFA E, SHAHROKHI M. Control of an anaerobic bioreactor using a fuzzy supervisory controller[J]. J Proc Control, 2021, 103: 87-99.

A motion performance-based assistance strategy for dual-person upper limb interaction

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