TY - CHAP
T1 - Monitoring patients during neurorehabilitation following central or peripheral nervous system injury
T2 - Dynamic difficulty adaptation
AU - Jelinek, Herbert F.
AU - Cornforth, David J.
AU - Koenig, Alexander
AU - Riener, Robert
AU - Karmakar, Chandan
AU - Imam, Md Hasan
AU - Khandoker, Ahsan H.
AU - Palaniswami, Marimuthu
AU - Minichiello, Mario
N1 - Publisher Copyright:
© 2018 by Taylor & Francis Group, LLC.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Brain injuries including stroke often require extensive cognitive and physical rehabilitation. Active mental engagement and a positive emotional state are prerequisites for optimal learning in the rehabilitation of stroke patients. Stroke often affects aspects of gait requiring balance and gait therapy using robot-assisted devices. Ideal cognitive and physical training conditions are an important prerequisite to obtain optimal robot-assisted therapeutic outcomes. Key factors for successful therapy include design of the rehabilitation task, attention to stress, and the psychological state of patients during robot-assisted gait therapy. Although the latter is difficult to gauge in real time, patient stress or anxiety can be inferred from heart rate variability (HRV). This chapter examines the design of robot-assisted therapy and the effect on HRV of increasing task difficulty. Learning to use a robot-assisted device for walking is influenced by the level of motivation or stress experienced by patients. If patients are overchallenged, they may withdraw and have difficulty learning the task. Psychological tests cannot be conducted while patients are strapped into the robot-assisted devices and hence alternative measures need to be considered to obtain 282information in real time on cognitive and psychological function. The regulation of heart rate by the autonomic nervous system is characterized by reciprocal connections to the cortex and deeper cerebral hemisphere (subcortical) structures and thus measures of HRV can be used as an indicator of cognitive involvement. Using our new method, we process the psychological state data in real time. We introduce HRV analysis as a first step toward real-time, auto-adaptive gait training with management of subject engagement. Our method has the potential to improve rehabilitation results by optimally challenging the patient at all stages of neurorehabilitation.
AB - Brain injuries including stroke often require extensive cognitive and physical rehabilitation. Active mental engagement and a positive emotional state are prerequisites for optimal learning in the rehabilitation of stroke patients. Stroke often affects aspects of gait requiring balance and gait therapy using robot-assisted devices. Ideal cognitive and physical training conditions are an important prerequisite to obtain optimal robot-assisted therapeutic outcomes. Key factors for successful therapy include design of the rehabilitation task, attention to stress, and the psychological state of patients during robot-assisted gait therapy. Although the latter is difficult to gauge in real time, patient stress or anxiety can be inferred from heart rate variability (HRV). This chapter examines the design of robot-assisted therapy and the effect on HRV of increasing task difficulty. Learning to use a robot-assisted device for walking is influenced by the level of motivation or stress experienced by patients. If patients are overchallenged, they may withdraw and have difficulty learning the task. Psychological tests cannot be conducted while patients are strapped into the robot-assisted devices and hence alternative measures need to be considered to obtain 282information in real time on cognitive and psychological function. The regulation of heart rate by the autonomic nervous system is characterized by reciprocal connections to the cortex and deeper cerebral hemisphere (subcortical) structures and thus measures of HRV can be used as an indicator of cognitive involvement. Using our new method, we process the psychological state data in real time. We introduce HRV analysis as a first step toward real-time, auto-adaptive gait training with management of subject engagement. Our method has the potential to improve rehabilitation results by optimally challenging the patient at all stages of neurorehabilitation.
UR - http://www.scopus.com/inward/record.url?scp=85054285938&partnerID=8YFLogxK
U2 - 10.4324/9781315372921
DO - 10.4324/9781315372921
M3 - Chapter
AN - SCOPUS:85054285938
SN - 9781482243475
SP - 281
EP - 296
BT - ECG Time Series Variability Analysis
ER -
