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Interdisciplinary collaboration the present and the future - the role of computer science in physiotherapy

Mikołajewska Emilia, Mikołajewski Dariusz

Studia i Materiały Informatyki Stosowanej

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2024

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T. 16, nr 2

13--20

EN

IT methods and tools are increasingly used in physiotherapy diagnosis and therapy, as well as in the care and monitoring of patients in telerehabilitation and home rehabilitation, and physiotherapy itself is becoming increasingly interdisciplinary. The aim of the article is to review to what extent the opportunities related to the interdisciplinary development of physiotherapy have been used and how much potential there is for further, stimulated development.

PL

Metody i narzędzia informatyczne znajdują coraz szersze zastosowanie w diagnostyce i terapii fizjoterapeutycznej oraz opiece i monitorowaniu pacjentów w ramach telerehabilitacji i rehabilitacji domowej, a sama fizjoterapia staje się coraz bardziej interdyscyplinarna. Celem artykułu jest przegląd, na ile możliwości związane z interdyscyplinarnym rozwojem fizjoterapii zostały wykorzystane, a ile w nich tkwi jeszcze potencjału na dalszy, stymulowany rozwój.

2

Can data processing algorithms ensure sufficient accuracy to estimate human body pose via wearable systems with use of IMU sensors? – an experimental evaluation

Szczerba Aleksandra, Prochor Piotr, Piszczatowski Szczepan

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2024

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Vol. 72, nr 1

art. no. e148835

EN

Background: The aim of the study was to answer two questions: 1 – Can data processing algorithms ensure sufficient accuracy for estimating human body pose via wearable systems? 2 – How to process the IMU sensor data to obtain the most accurate information on the human body pose? To answer these questions, the authors evaluated proposed algorithms in terms of accuracy and reliability. Methodology: data acquisition was performed with tested IMU sensors system mounted onto a Biodex System device. Research included pendulum movement with seven angular velocities (10-120°/s) in five angular movement ranges (30-120°). Algorithms used data from accelerometers and gyroscopes and considered complementary and/or Kalman filters with adjusted parameters. Moreover, angular velocity registration quality was also taken into consideration. Results: differences between means for angular velocity were 0.55÷1.05°/s and 1.76÷3.11%. In the case of angular position relative error of means was 4.77÷10.84%, relative error of extreme values was 2.15÷4.81% and Spearman’s correlation coefficient was 0.74÷0.89. Conclusions: Algorithm calculating angles based on acceleration-derived quaternions and with implementation of Kalman filter was the most accurate for data processing and can be adapted for future work with IMU sensors systems, especially in wearable devices that are designated to support human in daily activity.

3

An accurate and stable pose estimation method for planar cases considering the line constraints between every two points

Zhang Zimiao, Zhang Hao, Zhang Fumin, Zhang Shihai

Metrology and Measurement Systems

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2023

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Vol. 30, nr 2

235--258

EN

The current solutions for pose estimation problems using coplanar feature points (PnP problems) can be divided into non-iterative and iterative solutions. The accuracy, stability, and efficiency of iterative methods are unsatisfactory. Therefore, non-iterative methods have become more popular. However, the non-iterative methods only consider the correspondence of the feature points with their 2D projections. They ignore the constraints formed between feature points. This results in lower pose estimation accuracy and stability. In this work, we proposed an accurate and stable pose estimation method considering the line constraints between every two feature points. Our method has two steps. In the first step, we solved the pose non-iteratively, considering the correspondence of the 3D feature points with their 2D projections and the line constraints formed by every two feature points. In the second step, the pose was refined by minimizing the re-projection errors with one iteration, further improving accuracy and stability. Simulation and actual experiment results show that our method’s accuracy, stability, and computational efficiency are better than the other existing pose estimation methods. In the -45° to +45° measuring range, the maximum angle measurement error is no more than 0.039°, and the average angle measurement error is no more than 0.016°. In the 0 mm to, 30 mm measuring range, the maximum displacement measurement error is no more than 0.049 mm, and the average displacement measurement error is no more than 0.012 mm. Compared to other current pose estimation methods, our method is the most efficient based on guaranteeing measurement accuracy and stability.

4

An RDF-based action recognition framework with feature selection capability, considering therapy exercises utilizing depth cameras

Negin F., Akgül C.B., Yüksel K.A., Erçil A.

Journal of Theoretical and Applied Computer Science

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2014

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Vol. 8, nr 3

3--22

EN

Emerging cost-efficient depth sensor technologies reveal new possibilities to cope with difficulties in action recognition. Depth information improves the quality of skeleton detection process, hence, pose estimation can be done more efficiently. Recently many studies focus on temporal analyses over estimated skeleton poses to recognize actions. In this paper we have an inclusive study of the spatiotemporal kinematic features and propose an action recognition framework with feature selection capability to deal with the multitudinous of features by leveraging data mining capabilities of random decision forests. We describe human motion via a rich collection of kinematic feature time-series computed from the skeletal representation of the body in motion. We discriminatively optimize a random decision forest model over this collection to identify the most effective subset of features, localized both in time and space. Later, we train a support vector machine classifier on the selected features. This approach improves upon the baseline performance obtained using the whole feature set with a significantly less number of features (one tenth of the original). To justify our method we test the framework on various datasets and compared it with state-of-theart. On MSRC-12 dataset [25] (12 classes), our method achieves 94% accuracy. On the WorkoutSU-10 dataset [28], collected by our group (10 physical exercise classes), the accuracy is 98%. On MSR Action3D dataset [9] (20 classes) we obtain 87% average accuracy and for UTKinect-Action dataset [10] (10 classes) the accuracy is 92%. Other than regular activities, we also tried our approach to detect a falling person using the dataset which we recorded as an extension to our original dataset. We test how our method adjusts on different types of actions and we obtained promising results for this type of action. We discuss that our approach provides insights on the spatiotemporal dynamics of human actions and can be used to as part of different applications especially for rehabilitation of patients.

5

Pose Prediction Based on ECV Visual System Using Kalman Filter

Kozioł P., Miatliuk K., Buch A. G., Kruger N.

Machine Dynamics Research

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2011

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Vol. 35, No. 3

37-45

EN

This paper presents a study which uses an early cognitive vision system for model-based pose estimation of an object in motion. The estimated poses are processed by a Kalman filter which makes robotic manipulation possible. Results from a simulated robot environment are given.

6

Orientation and Pose estimation for Panoramic Imagery

Laganiere R., Kangni F.

Machine Graphics and Vision

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2010

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Vol. 19, No. 3

339-363

EN

In a database of geo-referenced images, determining the exact position of each panorama is an important step in order to ensure the consistency of visual information. This paper addresses the problem of camera pose recovery from spherical (360°) panoramas. The 3D information is extracted from a database of panoramic images sparsely distributed over a scene of interest. We present a two-stage algorithm to recover the position of omni-directional cameras using pair wise essential matrices. First, all rotations with respect to the world frame are found using an incremental bundle adjustment procedure, thus achieving what we call panorama alignment. Full camera positions are then computed using bundle adjustment. During this step, the previously computed panorama orientations, used to feed the global optimization process, can be further refined. Results are shown for indoor and outdoor panorama sets.

7

Estimation of pose parameters from a set of least square objective functions

Hati S.

Machine Graphics and Vision

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2008

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Vol. 17, No. 3

299-312

EN

In this paper, we have attempted to solve the pose estimation problem for a 3-dimensional object by independently estimating the pose parameters through the minimization of a set of objective, functions, using Gauss approximation techniques for least squares optimization. In our implementation, the 3-D object is assumed to have three degrees of freedom on a flat surface, which is typical of automated visual inspection applications. However, the solution can be also extended to greater degrees of freedom. We have is shown that the pose can be estimated by only considering the x-coordinates of the known vertices in the projected space, but the same is not true if we consider the y-coordinates alone. We propose a set of modified objective functions from which it is possible to find the pose parameters. The parameters have been determined in noisy conditions under 20-dB and 40-dB SNR values and the robustness of the estimators is confirmed.

8

Estimation of platform spatial pose and displacement of parallel mechanism using wire-based sensors

Góra M., Knapczyk J., Maniowski M.

Archive of Mechanical Engineering

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2007

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Vol. LIV, nr 4

365-389

EN

This paper presents an estimation method for the spatial pose and displacement parameters of multi-rod suspension mechanism, based on measurement results by using wire sensors. Some changes of position and orientation of the platform fixed to wheel knuckle cause corresponding changes of sensors' cable lengths. The fixation points of the cable sensors are selected with the collision-free conditions taken into account. Numerical example deals with platform poses and positioning of the sensors that satisfy the measurement conditions.

PL

W artykule przedstawiono metodę estymacji parametrów przestrzennego położenia i przemieszczenia mechanizmu wielo-wahaczowego zawieszenia koła samochodu na podstawie wyników pomiarów za pomocą czujników linkowych. Pewne zmiany pozycji i orientacji platformy zamocowanej do zwrotnicy koła powodują odpowiednie zmiany długości linek czujników. Punkty zaczepienia czujników linkowych dobrano uwzględniając warunki uniknięcia kolizji. Przykład liczbowy dotyczy analizy położeń i przemieszczeń platformy oraz usytuowania czujników dające dobre uwarunkowania pomiarów.

9

Real-time face detection and pose estimation for driver monitoring

Guo Z., Yan Y., Hua J., Yang J.

Image Processing & Communications

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2006

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Vol. 11, no 2

83-96

EN

For a driver monitoring system, one of the most important problems to solve is face detection rapidly. This paper presents an efficient approach to achieve fast and accurate face detection in gray level videos. Candidates of face at different scales are selected by finding regions based on Mask Transform (MT). To obtain real one, all the face candidates are then verified by using support vector machines (SVMs) based on Multi-scale 2D Walsh-Hadamard features. Head pose is estimated on the basis of accurate face detection. At last, we analyzed the head pose by a kind of Bilateral-projection Matrix Principle Component Analysis (BMPCA) algorithm proposed. Experimental results on many videos show that the algorithm can detect driver's face rapidly and estimate the head pose accurately. The proposed method is robust to deal with illumination changes, glasses wearing and different head pose with moderate rotations. Experimental results demonstrate its effectiveness.