@article{MTMT:3188496,
	title = {Inter-joint coordination deficits revealed in the decomposition of endpoint jerk during goal-directed arm movement after stroke.},
	url = {https://m2.mtmt.hu/api/publication/3188496},
	author = {Laczkó, József and Scheidt, RA and Simo, LS and Piovesan, D},
	doi = {10.1109/TNSRE.2017.2652393},
	journal-iso = {IEEE T NEUR SYS REH},
	journal = {IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING},
	volume = {25},
	unique-id = {3188496},
	issn = {1534-4320},
	abstract = {It is well-documented that neurological deficits after stroke can disrupt motor control processes that affect the smoothness of reaching movements. The smoothness of hand trajectories during multi-joint reaching depends on shoulder and elbow joint angular velocities and their successive derivatives as well as on the instantaneous arm configuration and its rate of change. Right-handed survivors of unilateral hemiparetic stroke and neurologically-intact control participants held the handle of a two-joint robot and made horizontal planar reaching movements. We decomposed endpoint jerk into components related to shoulder and elbow joint angular velocity, acceleration, and jerk. We observed an abnormal decomposition pattern in the most severely impaired stroke survivors consistent with deficits of inter-joint coordination. We then used numerical simulations of reaching movements to test whether the specific pattern of inter-joint coordination deficits observed experimentally could be explained by either a general increase in motor noise related to weakness or by an impaired ability to compensate for multi-joint interaction torque. Simulation results suggest that observed deficits in movement smoothness after stroke more likely reflect an impaired ability to compensate for multi-joint interaction torques rather than the mere presence of elevated motor noise.},
	year = {2017},
	eissn = {1558-0210},
	pages = {798-810}
}

@article{MTMT:32035499,
	title = {Control of cycling limb movements: Aspects for rehabilitation},
	url = {https://m2.mtmt.hu/api/publication/32035499},
	author = {Laczkó, József and Percze-Mravcsik, Mariann and Katona, Péter},
	doi = {10.1007/978-3-319-47313-0_15},
	journal-iso = {ADV EXP MED BIOL},
	journal = {ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY},
	volume = {957},
	unique-id = {32035499},
	issn = {0065-2598},
	year = {2016},
	eissn = {2214-8019},
	pages = {273-289}
}

@article{MTMT:3040236,
	title = {Prediction of muscle activity during loaded movements of the upper limb.},
	url = {https://m2.mtmt.hu/api/publication/3040236},
	author = {Tibold, Róbert and Fuglevand, AJ},
	doi = {10.1186/1743-0003-12-6},
	journal-iso = {J NEUROENG REHABIL},
	journal = {JOURNAL OF NEUROENGINEERING AND REHABILITATION},
	volume = {12},
	unique-id = {3040236},
	issn = {1743-0003},
	abstract = {BACKGROUND: Accurate prediction of electromyographic (EMG) signals associated with a variety of motor behaviors could, in theory, serve as activity templates needed to evoke movements in paralyzed individuals using functional electrical stimulation. Such predictions should encompass complex multi-joint movements and include interactions with objects in the environment. METHODS: Here we tested the ability of different artificial neural networks (ANNs) to predict EMG activities of 12 arm muscles while human subjects made free movements of the arm or grasped and moved objects of different weights and dimensions. Inputs to the trained ANNs included hand position, hand orientation, and thumb grip force. RESULTS: The ability of ANNs to predict EMG was equally as good for tasks involving interactions with external loads as for unloaded movements. The ANN that yielded the best predictions was a feed-forward network consisting of a single hidden layer of 30 neural elements. For this network, the average coefficient of determination (R2 value) between predicted and actual EMG signals across all nine subjects and 12 muscles during movements that involved episodes of moving objects was 0.43. CONCLUSION: This reasonable accuracy suggests that ANNs could be used to provide an initial estimate of the complex patterns of muscle stimulation needed to produce a wide array of movements, including those involving object interaction, in paralyzed individuals.},
	year = {2015},
	eissn = {1743-0003}
}

@article{MTMT:2770129,
	title = {The combined effect of cycling cadence and crank resistance on hamstrings and quadriceps muscle activities during cycling},
	url = {https://m2.mtmt.hu/api/publication/2770129},
	author = {Katona, Péter and Pilissy, Tamás and Tihanyi, Attila and Laczkó, József},
	doi = {10.1556/APhysiol.101.2014.4.12},
	journal-iso = {ACTA PHYSIOL HUNG},
	journal = {ACTA PHYSIOLOGICA HUNGARICA},
	volume = {101},
	unique-id = {2770129},
	issn = {0231-424X},
	year = {2014},
	eissn = {1588-2683},
	pages = {505-516},
	orcid-numbers = {Pilissy, Tamás/0000-0003-2908-4300}
}