122 research outputs found

    Preliminary evaluation of SensHand V1 in assessing motor skills performance in Parkinson Disease

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    Nowadays, the increasing old population 65+ as well as the pace imposed by work activities lead to a high number of people that have particular injuries for limbs. In addition to persistent or temporary disabilities related to accidental injuries we must take into account that part of the population suffers from motor deficits of the hands due to stroke or diseases of various clinical nature. The most recurrent technological solutions to measure the rehabilitation or skill motor performance of the hand are glove-based devices, able to faithfully capture the movements of the hand and fingers. This paper presents a system for hand motion analysis based on 9-axis complete inertial modules and dedicated microcontroller which are fixed on fingers and forearm. The technological solution presented is able to track the patients' hand motions in real-time and then to send data through wireless communication reducing the clutter and the disadvantages of a glove equipped with sensors through a different technological structure. The device proposed has been tested in the study of Parkinson's disease

    Using wearable sensor systems for objective assessment of parkinson's disease

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    This paper presents a novel wearable sensor system based on the integration of miniaturised IMUs for fine hand movement analysis. The system, named SensHand V1, is composed of full 9-axis inertial sensors, placed on the fingers and wrist, which are managed by a cortex-M3 microcontroller. The acquired data are sent to a data logger through the use of Bluetooth communication. In this paper, the system is used for the objective diagnosis of Parkinson's disease, which is commonly assessed by neurologists through visual examination of motor tasks and semi-quantitative rating scales. Here, these motor tasks are also assessed using the SensHand V1, and then compared with the subjective metrics. Results demonstrate that the system is adequate to support neurologists in diagnostic procedures and allows for an objective evaluation of the disease

    Empowering patients in self-management of parkinson's disease through cooperative ICT systems

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    The objective of this chapter is to demonstrate the technical feasibility and medical effectiveness of personalised services and care programmes for Parkinson's disease, based on the combination of mHealth applications, cooperative ICTs, cloud technologies and wearable integrated devices, which empower patients to manage their health and disease in cooperation with their formal and informal caregivers, and with professional medical staff across different care settings, such as hospital and home. The presented service revolves around the use of two wearable inertial sensors, i.e. SensFoot and SensHand, for measuring foot and hand performance in the MDS-UPDRS III motor exercises. The devices were tested in medical settings with eight patients, eight hyposmic subjects and eight healthy controls, and the results demonstrated that this approach allows quantitative metrics for objective evaluation to be measured, in order to identify pre-motor/pre-clinical diagnosis and to provide a complete service of tele-health with remote control provided by cloud technologies. ยฉ 2016, IGI Global. All rights reserved

    Music normalizes visual and proprioceptive control of movement in Parkinson's disease

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    xiv, 147 leaves : ill. ; 29 cm. --The sensory control of movements has been shown to be impaired with Parkinsonโ€™s disease. I investigated the task, reach-to-eat, in which advancing of the limb towards a target is guided by vision and withdrawal of the grasped target to the mouth is guided by somatosensation (i.e., haptics and proprioception). Parkinsonโ€™s diseased subjects display an alteration in the balance of visual and proprioceptive guidance, such that they display increased visual fixation on the target prior to movement onset that persists following the grasp. Music therapy can normalize the balance between visual and proprioceptive guidance on the reach-to-eat task, as visual fixation with the target prior to movement onset is consistent with controls, and disengagement following grasp no longer differs from mild Parkinsonโ€™s disease subjects. These results are the first to demonstrate that music can have an ameliorating effect on the sensory impairments seen in the control of forelimb movements in Parkinsonโ€™s disease

    Technological advances in deep brain stimulation:Towards an adaptive therapy

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    Parkinson's disease (PD) is neurodegenerative movement disorder and a treatment method called deep brain stimulation (DBS) may considerably reduce the patientโ€™s motor symptoms. The clinical procedure involves the implantation of a DBS lead, consisting of multiple electrode contacts, through which continuous high frequency (around 130 Hz) electric pulses are delivered in the brain. In this thesis, I presented the research which had the goal to improve current DBS technology, focusing on bringing the conventional DBS system a step closer to adaptive DBS, a personalized DBS therapy. The chapters in this thesis can be seen as individual building blocks for such an adaptive DBS system. After the general introduction, the first two chapters, two novel DBS lead designs are studied in a computational model. The model showed that both studied leads were able to exploit the novel distribution of the electrode contacts to shape and steer the stimulation field to activate more neurons in the chosen target compared to the conventional lead, and to counteract lead displacement. In the fourth chapter, an inverse current source density (CSD) method is applied on local field potentials (LFP) measured in a rat model. The pattern of CSD sources can act as a landmark within the STN to locate the potential stimulation target. The fifth and final chapter described the last building block of the DBS system. We introduced an inertial sensors and force sensor based measurement system, which can record hand kinematics and joint stiffness of PD patients. A system which can act as a feedback signal in an adaptive DBS system

    ็›ธๅฏพ็š„้ก•่‘—ๆ€งใŒ่ค‡ๆ•ฐ่‚ขๅ”่ชฟ้‹ๅ‹•ใซๅŠใผใ™ๅฝฑ้Ÿฟ

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    ๆ—ฉๅคงๅญฆไฝ่จ˜็•ชๅท:ๆ–ฐ7951ๆ—ฉ็จฒ็”ฐๅคง

    Development and degeneration of the sensory control of reach-to-eat behaviour

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    xiv, 286 leaves : ill. ; 29 cmThe reach-to-eat movement, in which a hand is advanced towards a food item, shapes to grasp the food item, and withdrawals to place the food item into the mouth for eating, is a behaviour that is performed daily. The movement is controlled by two sensory systems, vision to guide hand advance and grasping, and somatosensation to guide hand withdrawal and mouth placement. The purpose of the present thesis was to examine how the sensory control of reaching-to-eat develops in infancy and degenerates following neurodegenerative disorder. The tight coupling of vision to hand advance and somatosensation to hand withdrawal has a developmental profile from six months to one year of age. That is, six-month-old infants rely on vision to advance their hand, grasp the target, and withdrawal the target to the mouth. By twelve months of age, infants display the adult pattern of coupling vision to hand advance and grasping. The tight coupling of vision to hand advance degenerates with basal ganglia disease, such that subjects with Parkinsonโ€™s disease and Huntingtonโ€™s disease show an overreliance on vision to guide hand advance for grasping and hand withdrawal for mouth placement. The results of the thesis demonstrate that efficient use of sensory control to guide motor behaviour is an important aspect of development that is disrupted by neurodegenerative disease

    ๋‹ค์ค‘์†๊ฐ€๋ฝ ๊ณผ์ œ ์ˆ˜ํ–‰ ์‹œ ์ธ๊ฐ„์˜ ๊ฐ๊ฐ ๋ฐ ์ธ์ง€ ์ฒ˜๋ฆฌ ๊ณผ์ •์˜ ์ •๋Ÿ‰ํ™”

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ์ฒด์œก๊ต์œก๊ณผ, 2021. 2. ๋ฐ•์žฌ๋ฒ”.The continuously varied states of human body and surrounding environment require instantaneous motor adaptations and the understanding of motor goal to achieve desired actions. These sensory and cognitive processes have been investigated as elements in motor control during last five decades. Specially, the task dependency on sensory and cognitive processes suggest the effects of movement properties in terms of environment situation and motor goal. However, these effects were mostly empirically summarized with the measurements of either neural activity or simple motor accomplishment unilaterally. The current thesis addresses the quantification of sensory and cognitive processes based on simultaneous measurements of brain activity and synergic motor performance during multi-digit actions with different movement properties. Multi-digit action as a representation of synergic movements has developed into a widespread agency to quantify the efficacy of motor control, as the reason applied in this thesis. In this thesis, multi-digit rotation and pressing tasks were performed with different movement directions, frequencies, feedback modalities, or task complexities. (Chapter 3) The changes of movement direction induced a decrease in motor synergy but regardless of which direction. (Chapter 4 and 5) Increased frequency of rhythmic movement reduced synergic motor performance associate with decreased sensory process and less efficient cognitive process. (Chapter 6) More comprehensive feedback modality improved synergic performance with increased sensory process. (Chapter 7) Increased movement complexity had a consistent but stronger effect as increased frequency on synergic performance and efficiency of cognitive process. These observations suggest that several movement properties affect the contributions of sensory and cognitive processes to motor control which can be quantified through either neural activity or synergic motor performance. Accordingly, those movement properties may be applied in the rehabilitation of motor dysfunction by developing new training programs or assistant devices. Additionally, it may be possible to develop a simplified while efficient method to estimate the contribution of sensory or cognitive process to motor control.์‹œ์‹œ๊ฐ๊ฐ์œผ๋กœ ๋ณ€ํ™”ํ•˜๋Š” ์‹ ์ฒด ์ƒํƒœ์™€ ์ฃผ๋ณ€ ํ™˜๊ฒฝ์˜ ์ƒํ˜ธ์ž‘์šฉ ์†์—์„œ ์•Œ๋งž์€ ์›€์ง์ž„์„ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ทธ์— ๋”ฐ๋ฅธ ์ฆ‰๊ฐ์ ์ธ ์šด๋™ ์ ์‘(motor adaptation) ๊ณผ์ •์™€ ๊ณผ์ œ ๋ชฉํ‘œ์— ๋Œ€ํ•œ ์ดํ•ด๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์ธ๊ฐ„์˜ ๊ฐ๊ฐ ๋ฐ ์ธ์ง€ ์ฒ˜๋ฆฌ๊ณผ์ •์€ ์šด๋™ ์ œ์–ด ๋ถ„์•ผ์˜ ์ค‘์š”ํ•œ ์š”์†Œ๋กœ ์—ฌ๊ฒจ์กŒ๋‹ค. ์„ ํ–‰์—ฐ๊ตฌ์— ๋”ฐ๋ฅด๋ฉด, ์šด๋™ ๊ณผ์ œ์— ๋”ฐ๋ผ ๋ณ€ํ™”ํ•˜๋Š” ๊ฐ๊ฐ ๋ฐ ์ธ์ง€ ์ฒ˜๋ฆฌ๊ณผ์ •์€ ์ฃผ๋ณ€ ํ™˜๊ฒฝ๊ณผ ๊ณผ์ œ์˜ ๋ชฉํ‘œ์— ๋”ฐ๋ผ ์›€์ง์ž„์˜ ํŠน์„ฑ์— ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค๊ณ  ๋ณด๊ณ ๋˜์–ด์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ์˜ํ–ฅ์€ ๋Œ€๋ถ€๋ถ„ ๋‹จ์ˆœํ•œ ์šด๋™๊ณผ์ œ ์ˆ˜ํ–‰ ๊ฒฐ๊ณผ ๋˜๋Š” ์ธก์ •๋œ ์‹ ๊ฒฝ ํ™œ๋™์— ์˜ํ•ด ๊ฒฝํ—˜์ ์œผ๋กœ ์š”์•ฝ๋œ ๊ฒฐ๊ณผ์— ๊ตญํ•œ๋˜์–ด ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ๋ฌธ์€ ๋‹ค์–‘ํ•œ ์›€์ง์ž„ ํŠน์„ฑ์„ ๊ฐ€์ง„ ๋‹ค์ค‘ ์†๊ฐ€๋ฝ ๊ณผ์ œ ์ˆ˜ํ–‰ ์‹œ, ๋‡Œ ํ™œ๋™ (Brain activity)๊ณผ ๋”๋ถˆ์–ด ์†๊ฐ€๋ฝ๋“ค ๊ฐ„์˜ ํ˜‘์‘์ ์ธ ์›€์ง์ž„์˜ ์ˆ˜ํ–‰ ๊ฒฐ๊ณผ๋ฅผ ๋™์‹œ ์ธก์ •ํ•˜์—ฌ ๊ณผ์ œ์˜ ํŠน์„ฑ์— ๋”ฐ๋ฅธ ๊ฐ๊ฐ ๋ฐ ์ธ์ง€ ์ฒ˜๋ฆฌ๊ณผ์ •์˜ ๋ณ€ํ™”๋ฅผ ๋ถ„์„ํ–ˆ๋‹ค. ๋‹ค์ค‘ ์†๊ฐ€๋ฝ ๊ณผ์ œ๋Š” ์šด๋™ ์ œ์–ด์˜ ์„ฑ๋Šฅ ํšจ์œจ์„ฑ์„ ์ •๋Ÿ‰ํ™”ํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ๋˜๋Š” ๋Œ€ํ‘œ์ ์ธ ๊ณผ์ œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋‹ค์–‘ํ•œ ์กฐ๊ฑด์˜ ์›€์ง์ž„ ๋ฐฉํ–ฅ, ์›€์ง์ž„์˜ ์ฃผ๊ธฐ๋นˆ๋„, ๊ฐ๊ฐ ํ”ผ๋“œ๋ฐฑ ์–‘์‹ ๋˜๋Š” ๊ณผ์ œ ๋‚œ์ด๋„์— ๋”ฐ๋ฅธ ๋‹ค์ค‘ ์†๊ฐ€๋ฝ ํšŒ์ „ ๋™์ž‘ ๋ฐ ํž˜ ์ƒ์„ฑ ๊ณผ์ œ๋ฅผ ์‚ฌ์šฉํ–ˆ๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋กœ๋Š”, (๋ฌธ๋‹จ 3) ์›€์ง์ž„ ๋ฐฉํ–ฅ์ด ๋ณ€ํ™”ํ•˜๊ธฐ ์ „์— ๋ณ€ํ™”ํ•  ๋ฐฉํ–ฅ์— ์ƒ๊ด€์—†์ด ํ˜‘์‘์ ์ธ ์›€์ง์ž„์ด ์•…ํ™”๋˜์—ˆ๋‹ค. (๋ฌธ๋‹จ 4์™€ 5) ์›€์ง์ž„์˜ ์ฃผ๊ธฐ๋นˆ๋„๊ฐ€ ์ฆ๊ฐ€ํ• ์ˆ˜๋ก ํ˜‘์‘์ ์ธ ์›€์ง์ž„์ด ์•…ํ™”๋์œผ๋ฉฐ, ์ด์™€ ๊ด€๋ จ๋œ ๊ฐ๊ฐ ๋ฐ ์ธ์ง€ ์ฒ˜๋ฆฌ๊ณผ์ •์˜ ํšจ์œจ์„ฑ๋„ ๊ฐ์†Œ๋˜์—ˆ๋‹ค. (๋ฌธ๋‹จ 6) ๋‹จ์ผ ๊ฐ๊ฐ ํ”ผ๋“œ๋ฐฑ ์ œ๊ณต์กฐ๊ฑด์— ๋น„ํ•ด ์ข…ํ•ฉ์ ์ธ ๊ฐ๊ฐ ํ”ผ๋“œ๋ฐฑ์€ ์ฆ๊ฐ€๋œ ๊ฐ๊ฐ ์ฒ˜๋ฆฌ๊ณผ์ •๊ณผ ํ•จ๊ป˜ ํ˜‘์‘์ ์ธ ์›€์ง์ž„์„ ํ–ฅ์ƒ์‹œ์ผฐ๋‹ค. (๋ฌธ๋‹จ 7) ๊ณผ์ œ์˜ ๋‚œ์ด๋„๊ฐ€ ์ฆ๊ฐ€ํ• ์ˆ˜๋ก ํ˜‘์‘์ ์ธ ์›€์ง์ž„๊ณผ ์ธ์ง€ ์ฒ˜๋ฆฌ๊ณผ์ •์˜ ํšจ์œจ์„ฑ์€ ๊ฐ์†Œ๋˜์—ˆ์œผ๋ฉฐ, ์›€์ง์ž„์˜ ์ฃผ๊ธฐ๋นˆ๋„ ์กฐ๊ฑด์— ๋น„ํ•ด ๊ณผ์ œ์˜ ๋‚œ์ด๋„์— ๋”ฐ๋ผ ํ˜‘์‘์ ์ธ ์›€์ง์ž„๊ณผ ์ธ์ง€ ์ฒ˜๋ฆฌ๊ณผ์ •์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์€ ์ƒ๋Œ€์ ์œผ๋กœ ๋” ํฌ๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ์›€์ง์ž„ ํŠน์„ฑ์— ๋”ฐ๋ฅธ ๋‡Œ ํ™œ๋™๊ณผ ํ˜‘์‘์ ์ธ ๊ณผ์ œ ์ˆ˜ํ•ด ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ์šด๋™ ์ œ์–ด ๊ณผ์ •์—์„œ ๊ฐ๊ฐ ๋ฐ ์ธ์ง€ ์ฒ˜๋ฆฌ๊ณผ์ •์˜ ๊ธฐ์—ฌ์ •๋„๋ฅผ ์ •๋Ÿ‰ํ™”ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ์›€์ง์ž„ ํŠน์„ฑ์— ๋”ฐ๋ฅธ ๊ฐ๊ฐ ๋ฐ ์ธ์ง€ ์ฒ˜๋ฆฌ ๊ณผ์ •์˜ ๊ธฐ์—ฌ์ •๋„์˜ ๋ณ€ํ™”๋Š” ์šด๋™ ๊ธฐ๋Šฅ ์žฅ์• ๋ฅผ ๊ฐ€์ง„ ์‚ฌ๋žŒ๋“ค์˜ ์ƒˆ๋กœ์šด ์žฌํ™œ ํ›ˆ๋ จ ํ”„๋กœ๊ทธ๋žจ ๋ฐ ์›€์ง์ž„ ๋ณด์กฐ ์žฅ์น˜๋ฅผ ๊ฐœ๋ฐœํ•˜๊ธฐ ์œ„ํ•œ ์‹คํ—˜์ ์ธ ๊ทผ๊ฑฐ๋กœ ์ ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ๊ฐ๊ฐ ๋˜๋Š” ์ธ์ง€ ๊ณผ์ •์ด ์šด๋™ ์ œ์–ด์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์ถ”์ •ํ•˜๊ธฐ ์œ„ํ•œ ํšจ์œจ์ ์ธ ๋ฐฉ๋ฒ•์„ ๊ฐœ๋ฐœํ•˜๋Š”๋ฐ ๋„์›€์ด ๋  ๊ฒƒ์ด๋‹ค.Chapter 1. Introduction 1 1.1 Problem statement 1 1.2 Study objective 2 1.3 Organization of dissertation 3 Chapter 2. Background 6 2.1 Motor system 6 2.1.1 Ascending pathway 6 2.1.2 Descending pathway 8 2.1.3 Brain networks 9 2.2 Motor synergy 11 2.2.1 Synergy in performance 12 2.2.2 Synergy in muscles 13 2.2.3 Synergy in neurons 14 2.3 Motor control 15 2.1.1 Sensory process 16 2.1.2 Cognitive process 19 Chapter 3. Effect of movement direction: Multi-Finger Interaction and Synergies in Finger Flexion and Extension Force Production 23 3.1 Abstract 23 3.2 Introduction 24 3.3 Method 28 3.4 Results 35 3.4.1 Maximal voluntary contraction (MVC) force and finger independency 36 3.4.2 Timing indices 37 3.4.3 Multi-finger synergy indices in mode space 39 3.4.4 Multi-finger synergy indices in force space 43 3.5 Discussion 44 3.5.1 Finger independency during finger flexion and extension 44 3.5.2 Multi-finger synergies in force and mode spaces 46 3.5.3 Anticipatory synergy adjustment 48 Chapter 4. Effect of Frequency: Brain Oxygenation Magnitude and Mechanical Outcomes during Multi-Digit Rhythmic Rotation Task 51 4.1 Abstract 51 4.2 Introduction 51 4.3 Methods 55 4.4 Results 61 4.4.1 PET imaging 61 4.4.2 Finger forces 62 4.4.3 UCM analysis 64 4.4.4 Correlation between neural activation and mechanics 65 4.5 Discussion 66 4.5.1 Regions involved in feedback 67 4.5.2 Regions involved in feedforward 69 4.5.3 Corporation of feedforward and feedback 71 4.6 Conclusions 72 Chapter 5. Effect of frequency: Prefrontal Cortex Oxygenation during Multi-Digit Rhythmic Pressing Actions using fNIRS 74 5.1 Abstract 74 5.2 Introduction 74 5.3 Method 77 5.4 Results 84 5.4.1 Performance 84 5.4.2 Multi-digit coordination indices 84 5.4.3 Functional connectivity (FC) 87 5.5 Discussion 88 5.6 Conclusion 91 Chapter 6. Effect of Sensory Modality: Multi-Sensory Integration during Multi-Digit Rotation Task with Different Frequency 92 6.1 Abstract 92 6.2 Introduction 92 6.3 Method 94 6.4 Results 100 6.4.1 Performance 100 6.4.2 Multi-digit coordination indices 101 6.5 Discussion 101 6.6 Conclusion 103 Chapter 7. Effect of Task Complexity: Prefrontal Cortex Oxygenation during Multi-Digit Pressing Actions with Different Frequency Components 104 7.1 Abstract 104 7.2 Introduction 104 7.3 Method 106 7.4 Results 112 7.4.1 Performance 112 7.4.2 Multi-finger coordination indices 113 7.4.3 Functional connectivity (FC) 114 7.5 Discussion 115 7.5.1 Relation between Frequency and task complexity 115 7.5.2 Cognitive process in motor control 116 7.5.3 Relation between motor coordination and cognitive process 118 7.6 Conclusion 119 Chapter 8. Conclusions and Future Work 120 8.1 Summary of conclusions 120 8.2 Future work 121 Bibliography 122 Abstract in Korean 160Docto
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