Towards a personal at-home lab for motion video tracking in patients with Parkinson's disease

Meigal, Alexander; Prokhorov, Kirill; Bazhenov, Nikita; Gerasimova-Meigal, Liudmila; Korzun, Dmitry

doi:10.23919/fruct.2017.8250187

Cited by 13 publications

(2 citation statements)

References 35 publications

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“…Inmoststudies,wearablesensorsweremountedonmajorsegmentsofahumanbody (Ossiget al,2016;Walgaard,Faber,vanLummel,vanDieën,&Kingma,2016;Silsupadol,Teja,&Lugade, 2017).Forexample,upto17inertialsensorswereattachedtoextremitiesandlowerbacktoprovide betteroutcome (Nguyenetal.,2017).However,Prateeketal. (2017)proposedtouseonlyonesensor fixedonthetopofahead.Similarly,reliablespatiotemporaltrajectoryoftheheadwasobtainedwith thehelpofsinglelightreturningsphere (Meigal,Prokhorov,Gerasimova-Meigal,Bazhenov,&Korzun, 2017).Thus,sensorattachedtotheheadwouldhaveprovidedreliablesignalforgatedescription.…”

Section: Introductionmentioning

confidence: 99%

Using Smartphone Inertial Measurement Unit for Analysis of Human Gait

Reginya

Meigal

Gerasimova-Meigal

et al. 2019

International Journal of Embedded and Real-Time Communication Systems

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The study was aimed at searching the characteristic features of a human gait during a conventionally used neurologic walking test with the help of sensors (3D accelerometer and gyroscope) in a smartphone mounted on the person's head. This allowed reducing the amount of analyzed data and saving time for analysis in comparison with motion video capture methods. It has been found that merely one inertial unit is good enough to detect the gait left-right asymmetry in healthy subjects. Several parameters were derived, one of them (acceleration shock) proved to be the most informative one. The gyroscope signal allowed detecting the characteristics of the gait in Parkinson's disease patients seen as the excess of the power spectrum density in all three axes. Therefore, this study was aimed to extract amplitude and spectral parameters from acceleration and rotation rate signals of smartphone-based IMU attached to the head during the long version of the TUG test in a group of neurologically healthy young subjects.

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Section: Introductionmentioning

confidence: 99%

Using Smartphone Inertial Measurement Unit for Analysis of Human Gait

Reginya

Meigal

Gerasimova-Meigal

et al. 2019

International Journal of Embedded and Real-Time Communication Systems

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“…A complete home-based solution for motion video capture technique is tested in [85]. Authors study motor function in humans by assessing joint angles, position,velocity, and acceleration of a limb in 3D.…”

Section: Symptoms Detectionmentioning

confidence: 99%

Modeling human behaviour to improve retail efficiency and health monitoring

González¹

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This dissertation presents a model to express the most relevant features of the human behaviour, specifying two personalized instances for retail and health Monitoring domains. These features are divided into static and dynamic. For the static ones facial features are the most relevant since they can express different attributes of the subjects such as age, gender, emotions ... For that purpose landmarks and pose are the most significant points of interest. However, the body pose can also reveal some interesting features of the current state of one person and should also be taken into account. Regarding the dynamic properties, the trajectories described by a subject in the monitored scenario can also express very useful insights. It is a basic task to delimit the area where the subject is located that is mandatory to perform the static inferences. The interactions of the user with the environment are a very relevant feature, and should be modeled as well. Thus, initial model has been evolved for the domains previously mentioned. Retail is a scenario where the shoppers express a large amount of features that should be considered by the retailers. Concepts such as density of the areas of the establishment, focus of attention, interactions with the shelves, queues management or characterization of the visitors are included in the model to improve the efficiency of the physical shops. A completely different field such as health monitoring could lead to a completely different approach. But the trajectories of the patients, their facial properties, their interactions or their body pose are also meaningful in this domain to assist decision-making of the health professionals. In our work we have mainly studied the behaviour of the patients with cognitive diseases. This field requires monitoring in different scenarios such as the home of the patient, the clinic centre or the rehabilitation centre. A scalabe system and its deployment is proposed to provide an accurate monitoring. In this area we shall also include medical features such as the treatment of the patients, their body pose evolution in physiotherapy sessions or measurements such as their body temperature or their heart rate. Stated the architecture of every domain, we have gone beyond in two Computer Vision and Machine Learning tasks: human pedestrian tracking and facial analysis. For the first we have developed two solutions that have been tested on real scenarios and on controlled environments that are suitable for a deeper analysis. Feature descriptors such as HOG or LBP have been tested, and Machine Learning classifiers such as Adaboost or Support Vector Machines have been optimized. We propose the best combination for previously exposed techniques and we show results on the different scenarios proposed to validate our proposal. A 3D visual imaging system is optimized by the proposal of a multi-camera RGB-D system able to capture facial properties at extreme poses. The data collection gathered allow 3D reconstruction of facial areas of 92 subjects captured,...

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