20.500.12556/RUL-112589 Personalizirana senzorno in robotsko podprta vadba za zgornje ude Personalized sensor- and robot-supported training for upper limbs Spremljanje gibalnih sposobnosti zgornjih udov je pomembno skozi celo obdobje rehabilitacije. V doktorski disertaciji, ki jo sestavljajo tri študije, obravnavamo analizo dejavnosti zgornjih udov v kliničnem okolju, pri vsakodnevnih aktivnostih ter ocenjevanje interakcije zgornjih udov med vadbo z robotom. Za potrebe raziskav smo razvili nosljiv merilni sistem, ki sestoji iz magnetoinercialnih merilnih enot (IME) in senzorjev mišične aktivnosti. Senzorji so majhni in med uporabo ne ovirajo gibanja zgornjih udov. Predstavljen je postopek izračuna kinematike zgornjih udov iz podatkov orientacije IME. Prva študija se osredotoča na ocenjevanje gibov, kot jih definirata dva standardna klinična testa, ARAT in WMFT. Testa sta bila izvedena na pacientih po možganski kapi ter na zdravih prostovoljcih. Med izvajanjem testov je imel preiskovanec na zgornjih udih nameščen nosljivi merilni sistem. V študiji smo za analizo gibanja izbrali štiri kinematične parametre (premik trupa, čas trajanja giba, gladkost giba, trajektorija gibanja glede na trajektorije zdravih preiskovancev) ter parameter za ocenjevanje sile prijema, izračunan na podlagi mišične aktivnosti med izvajanjem nalog ARAT. Posamezne naloge so bile z metodo segmentacije razdeljene na faze manipulacije s predmetom in faze gibanja, za katere so izračunani našteti parametri ocenjevanja. Pacienti so bili za statistično analizo razdeljeni v skupine glede na oceno kliničnih testov. Na osnovi predlaganih parametrov je mogoče razlikovati med različnimi skupinami pacientov. Veljavnost parametrov je bila preverjena s primerjavo z ARAT oceno in kaže močno korelacijo pri parametrih časa trajanja giba ter gladkosti giba. Cilj rehabilitacije pacientov po možganski kapi je ponovno učenje gibov, potrebnih za izvajanje vsakodnevnih dejavnosti. V drugi študiji v okviru disertacije je bil tako nosljivi merilni sistem uporabljen za spremljanje gibanja zgornjih udov med izvajanjem vsakodnevnih dejavnosti. V prvem koraku smo analizo gibanja izvedli na osnovi časovne kvantizacije meritev in izračuna kvantov aktivnosti, kvantov mišične aktivnosti ter kvantov moči posameznega uda. Časovna kvantizacija omogoča primerjavo dejavnosti zgornjih udov že v kratkih časovnih intervalih. V drugem koraku smo analizo gibanja osnovali na metodi segmentacije, s katero preko sprememb hitrosti in smeri gibanja zgornjega uda, gibanje razdelimo na posamezne gibe. Na osnovi segmentacije smo analizirali dolžino poti giba roke, doseženo višino roke, kote v sklepih in mišično aktivnost. S parametri je mogoče razlikovati med dejavnostjo enega in drugega uda. Vpeljali smo parameter za ocenjevanje koordinacije gibanja, na osnovi katerega je mogoče ločevati med enoročnimi in dvoročnimi dejavnostmi. V tretji študiji je bila izvedena analiza interakcije zgornjih udov med dvoročnim izvajanjem nalog z robotom. Predstavili smo robotski sistem, naloge v navideznem okolju in uvedli metode izračuna parametrov. Sistem smo uporabili s skupino zdravih preiskovancev in skupino pacientov po možganski kapi. Analizo smo zasnovali na meritvah sil interakcije med rokama in robotom. Izvedli smo primerjavo med skupinami preiskovancev pri določenem uporu robota in primerjavo vpliva upora robota znotraj posamezne skupine preiskovancev. Predlagana metoda omogoča kvantifikacijo aktivnosti posameznega uda in razlikovanje med stopnjami okvare. Analiza vpliva upora robota je v veliki meri pokazala statistično značilne razlike za večino izračunanih parametrov znotraj skupine zdravih preiskovancev, v manjˇsi meri pa tudi znotraj skupin pacientov. Monitoring of upper limbs motor skills is important throughout the rehabilitation process. The doctoral thesis, consisting of three studies, deals with the analysis of upper limb activity in the clinical setting, during activities of daily living, and the evaluation of upper limb interaction during robot training. Upper limb movement was measured with a wearable sensory system consisting of wireless inertial-magneto measurement units (IMU) and electromyography sensors. Sensors are small and do not interfere with upper limb movement. The methodology for computation of upper limb kinematics based on IMU data is presented. First study focuses on measuring and quantifying upper limb and trunk movement while executing ARAT and WMFT motor tasks. Equipped with wearable sensory system, patients after stroke and healthy volunteers executed tasks of clinical tests according to the standard protocol. The movement was quantified with five parameters that are associated with clinical assessment: movement time, movement smoothness, similarity of hand trajectories, trunk stability, and fingers and wrist muscle activity. Tasks were segmented into object manipulation phases and movement phases, for which the five parameters were computed. Data were allocated into four groups. Patients who suffered stroke were grouped based on their clinical scores obtained for each task. Based on the proposed parameters, it is possible to differentiate between groups of patients. Numerical quantification of movement was additionally compared to the total ARAT scores obtained by each patient, and shows strong correlation for movement time and movement smoothness. Throughout the rehabilitation process patients after stroke need to re-learn movements for performing activities of daily living. In the second study of the doctoral thesis we used wearable sensory system for monitoring upper limbs movement while performing activities of daily living. In the first step, time quantization of movement is used for computation of activity counts, counts of muscle activity and power counts for each upper limb. Time quantization allows comparison of upper limb activities within short time intervals. In the second step, upper limb motion was segmented into individual movements based on changes in velocity and direction of movement of the upper limbs. On the basis of segmentation, we analysed path length of the hand movement, the achieved hand height, joint angles and muscle activity. The parameters can be used to distinguish between the activity of one and the other limb. We introduced a parameter for estimating movement coordination, based on which it is possible to distinguish between unimanual and bimanual activities. In the third study, the analysis of the interaction of the upper limbs during a bimanual tasks with a robot was performed. Robot system, tasks in virtual environment, and methodology for computation of movement parameters are presented. The system was used with a group of healthy volunteers and a group of patients after stroke. The analysis was based on measurements of the interaction forces between the upper limbs and the robot. We performed a comparison between groups of subjects at a given robot resistance and a comparison of the impact of robot resistance within each group of subjects. The proposed method enables quantification of activities of each upper limb and differentiates between the groups with different degrees of impairment. Analysis of robot resistance shows to a large extent statistical significant differences for most of the computed parameters within the group of healthy subjects and to lesser extent within the groups of patients after stroke. nosljivi merilni sistemi inercialne merilne enote elektromiografija rehabilitacijska robotika možganska kap rehabilitacija vsakodnevne aktivnosti ocenjevanjegibalnih sposobnosti zgornjih udov ARAT WMFT. wearable measuring systems inertial measuring units electromyography rehabilitation robotics stroke rehabilitation activities of daily living assessment ofupper limb functions ARAT WMFT. true false false Slovenski jezik Angleški jezik Doktorsko delo/naloga 2019-10-25 08:50:01 2019-10-25 08:50:14 2022-08-24 03:51:53 0000-00-00 00:00:00 2019 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 49481 12744788 Cebasek_Eva_-_PERSONALIZIRANA_SENZORNO_IN_ROBOTSKO_PODPRTA_VADBA_ZA_ZGORNJE_UDE.pdf Cebasek_Eva_-_PERSONALIZIRANA_SENZORNO_IN_ROBOTSKO_PODPRTA_VADBA_ZA_ZGORNJE_UDE.pdf 1 8294A22E27B8277393F83A9D916AFC60 2730496f3b22df016ea2e6e9c9d13aad17ff85344d8b23516516a9caa58bee33 ea7af4ee-a1b7-11eb-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=124505 Fakulteta za elektrotehniko 0 0 0