Inertial sensors signal processing methods for gait analysis of patients with impaired gait patterns

Analiza hoda je postala široko rasprostranjen klinički alat koji se koristi za objektivnu evaluaciju obrasca hoda, efekata hirurških intervencija, oporavka ili efekata terapije. Sve veći broj kliničara bira pogodne tretmane za lečenje pacijenata na osnovu informacija o kinematici i kinetici hoda. Procena i kvantifikacija parametara hoda je važan zahtev u oblasti ortopedije i rehabilitacije, ali takođe i u sportu, rekreaciji i posebno u razvoju tehnologija za ljude u procesu starenja. U cilju objektivne procene obrasca hoda, razvijen je bežični senzorski sistem čije su senzorske jedinice bežične, malih dimenzija i jednostavno se montiraju na segmente nogu subjekta čiji se hoda analizira. Senzorske jedinice podržavaju 3D inercijalne senzore (senzore ubrzanja i ugaonih brzina, tj. akcelerometre i žiroskope), kao i senzore sile. Osnovni cilj istraživanja je doprinos metodologiji za obradu podataka sa inercijalnih senzora i razvoj novih metoda obrade signala sa inercijalnih senzora u procesu određivanja kinematičkih veličina koje su uobičajene u analizi hoda (uglovi u zglobovima, brzina kretanja, dužina koraka). Ova metodologija je od posebne važnosti za objektivnu procenu nivoa motornog deficita, progresa bolesti i efikasnosti terapija, kao i efikasnosti primenjene motorne kontrole (prilikom funkcionalne električne stimulacije). U toku istraživanja razvijeno je nekoliko metoda za računanje uglova segmenata nogu ili zglobova, u zavisnosti od senzorske konfiguracije i složenosti algoritma. U disertaciji su odvojeno prikazani slučajevi u kojima je neophodno posmatrati kretanje u prostoru (3D analiza) i mnogo češći slučaj kad se kinematika može redukovati na sagitalnu ravan (2D analiza). Algoritmi uključuju i kalibraciju senzora, eliminaciju viii drifta, rekonstrukciju trajektorije i izračunavanje niza drugih relevantnih podataka koji karakterišu obrazac hoda. Dobijeni rezultati su poređeni sa postojećim sistemima za analizu hoda koji su validirani za kliničke primene. (sistemi sa kamerama, goniometri, enkoderi)...Gait analysis has become a widely used clinical tool which provides objective evaluation of the gait pattern, the effects of surgical interventions, recovery or therapy progress, and more and more clinicians are choosing therapy treatments based on gait kinematics and kinetics. Measuring gait parameters is an important requirement in the orthopedic and rehabilitation fields, but also in sports and fitness, and development of technologies for elderly population. In order to provide objective evaluation of the gait pattern, we have developed sensor system with light and small wireless sensor units, which can be easily mounted on body. These sensor units comprise 3-D inertial sensors (accelerometers and gyroscopes) and force sensing resistors, and our recommended setup includes one sensor unit per each segment of both legs. The main goal of this research is contribution to the methodology for processing of signals from inertial sensors (accelerometer pairs, or accelerometer and gyroscope sensor units). By using signal processing algorithms developed for this research, inertial sensors allow objective assessment of the quality of the gait pattern. This methodology is especially important for assessment of the motor deficit, progress of the disease and therapy effectiveness, and effectiveness of performed motor control (functional electrical stimulation). We have developed several methods for estimation of leg segment angles and joint angles, which differ in sensor configuration and algorithm complexity. Methods based only on accelerometers offer reliable angle estimations, which are limited to sagittal plane analysis, while the method using accelerometers and gyroscopes allows 3- D analysis. All this algorithms include sensor calibration, drift minimization, trajectory x reconstruction and calculation of numerous other parameters relevant to gait pattern analysis. The obtained results were compared with other commercial systems which are validated for clinical applications (camera systems, goniometers, encoders)..

Kvantitativna i kvalitativna procena obrasca hoda kod bolesnika sa Parkinsonovom bolešću

Background/Aim. Postural impairments and gait disorders in Parkinson's disease (PD) affect limits of stability, impaire postural adjustment, and evoke poor responses to perturbation. In the later stage of the disease, some patients can suffer from episodic features such as freezing of gait (FOG). Objective gait assessment and monitoring progress of the disease can give clinicians and therapist important information about changes in gait pattern and potential gait deviations, in order to prevent concomitant falls. The aim of this study was to propose a method for identification of freezing episodes and gait disturbances in patients with PD. A wireless inertial sensor system can be used to provide follow-up of the treatment effects or progress of the disease. Methods. The system is simple for mounting a subject, comfortable, simple for installing and recording, reliable and provides high-quality sensor data. A total of 12 patients were recorded and tested. Software calculates various gait parameters that could be estimated. User friendly visual tool provides information about changes in gait characteristics, either in a form of spectrogram or by observing spatiotemporal parameters. Based on these parameters, the algorithm performs classification of strides and identification of FOG types. Results. The described stride classification was merged with an algorithm for stride reconstruction resulting in a useful graphical tool that allows clinicians to inspect and analyze subject's movements. Conclusion. The described gait assessment system can be used for detection and categorization of gait disturbances by applying rule-based classification based on stride length, stride time, and frequency of the shank segment movements. The method provides an valuable graphical interface which is easy to interpret and provides clinicians and therapists with valuable information regarding the temporal changes in gait.Uvod/Cilj. Poremećaji hoda i ravnoteže kod bolesnika sa Parkinsonovom bolešću (PD) uključuju i poremećaje stabilnosti, održavanja ravnoteže prilikom hoda i nemogućnost adekvatne reakcije na iznenadne perturbacije. U kasnijim fazama bolesti neki bolesnici razvijaju i epizode motornog bloka, odnosno 'frizing' tokom hoda. Objektivno praćenje i merenje karakteristika hoda i promena obrasca hoda tokom progresije bolesti mogu pomoći kliničarima jer ukazuju na promene koje bi dovele do padova i ugrozile bolesnika. Cilj rada bio je razvoj metode koja bi identifikovala ovakve epizode kod bolesnika sa Parkinsonovom bolesti. Razvijeni bežični sistem sa senzorima mogao bi se koristiti za posmatranje efekata terapije ili progresije bolesti. Metode. U radu je prikazan sistem za objektivnu procenu obrasca hoda. Korišćenjem bežičnog senzorskog sistema koji koristi akcelerometre, žiroskope i senzore sile, moguće je dobiti procenu parametara hoda, ali i identifikovati 'frizing' epizode karakteristične za PD. Uz pomoć ovog sistema snimljeno je 12 bolesnika, te je na osnovu snimljenih signala razvijen novi softverski alat koji omogućava praćenje parametara hoda. Rezultati. Na osnovu dužine koraka, trajanja koraka i frekvencije pokreta, razvijen je algoritam za klasifikaciju tipova koraka i uočavanje promena frekvencija pokreta tokom hoda. Prikaz rezultata ovog sistema je dat kroz primer jednog bolesnika. Zaključak. Opisani sistem za procenu hoda može biti korišćen za kategorizaciju poremećaja hoda kroz posmatranje promena u dužini i trajanju koraka, kao i frekvencija segmenata noge. Razvijeni metod omogućava iliustrativni prikaz i grafički interfejs koji je jednostavan za interpretaciju i omogućava dobijanje informacija koje kliničarima mogu ukazati na trenutne promene u obrascu hoda

Classification of walking patterns in Parkinson's disease patients based on inertial sensor data

The gait disturbances in Parkinson's disease (PD) patients occur occasionally and intermittently, appearing in a random, inexplicable manner. These disturbances include festinations, shuffling, and complete freezing of gait (FOG). Alternation of walking pattern decreases the quality of life and may result in falls. In order to recognize disturbances during walking in PD patients, we recorded gait kinematics with wireless inertial measurement system and designed an algorithm for automatic recognition and classification of walking patterns. The algorithm combines a perceptron neural network with simple signal processing and rule-based classification. In parallel, gait was recorded with video camera. Medical experts identified FOG episodes from videos and their results were used for comparison and validation of this method. The summary result shows that the error in recognition and classification of walking patterns is up to 16%