6 research outputs found
Estimation and validation of temporal gait features using a markerless 2D video system
Background and Objective: Estimation of temporal gait features, such as stance time, swing time and gait cycle time, can be used for clinical evaluations of various patient groups having gait pathologies, such as Parkinsonβs diseases, neuropathy, hemiplegia and diplegia. Most clinical laboratories employ an optoelectronic motion capture system to acquire such features. However, the operation of these systems requires specially trained operators, a controlled environment and attaching reflective markers to the patientβs body. To allow the estimation of the same features in a daily life setting, this paper presents a novel vision based system whose operation does not require the presence of skilled technicians or markers and uses a single 2D camera.
Method: The proposed system takes as input a 2D video, computes the silhouettes of the walking person, and then estimates key biomedical gait indicators, such as the initial foot contact with the ground and the toe off instants, from which several other temporal gait features can be derived.
Results: The proposed system is tested on two datasets: (i) a public gait dataset made available by CASIA, which contains 20 users, with 4 sequences per user; and (ii) a dataset acquired simultaneously by a marker-based optoelectronic motion capture system and a simple 2D video camera, containing 10 users, with 5 sequences per user. For the CASIA gait dataset A the relevant temporal biomedical gait indicators were manually annotated, and the proposed automated video analysis system achieved an accuracy of 99% on their identification. It was able to obtain accurate estimations even on segmented silhouettes where, the state-of-the-art markerless 2D video based systems fail. For the second database, the temporal features obtained by the proposed system achieved an average intra-class correlation coefficient of 0.86, when compared to the "gold standard" optoelectronic motion capture system.
Conclusions: The proposed markerless 2D video based system can be used to evaluate patientsβ gait without requiring the usage of complex laboratory settings and without the need for physical attachment of sensors/markers to the patients. The good accuracy of the results obtained suggests that the proposed system can be used as an alternative to the optoelectronic motion capture system in non-laboratory environments, which can be enable more regular clinical evaluations.info:eu-repo/semantics/acceptedVersio
Markerless Kinematics of Pediatric Manual Wheelchair Mobility
Pediatric manual wheelchair users face substantial risk of orthopaedic injury to the upper extremities, particularly the shoulders, during transition to wheelchair use and during growth and development. Propulsion strategy can influence mobility efficiency, activity participation, and quality of life. The current forefront of wheelchair biomechanics research includes translating findings from adult to pediatric populations, improving the quality and efficiency of care under constrained clinical funding, and understanding injury mechanisms and risk factors. Typically, clinicians evaluate wheelchair mobility using marker-based motion capture and instrumentation systems that are precise and accurate but also time-consuming, inconvenient, and expensive for repeated assessments. There is a substantial need for technology that evaluates and improves wheelchair mobility outside of the laboratory to provide better outcomes for wheelchair users, enhancing clinical data. Advancement in this area gives physical therapists better tools and the supporting research necessary to improve treatment efficacy, mobility, and quality of life in pediatric wheelchair users. This dissertation reports on research studies that evaluate the effect of physiotherapeutic training on manual wheelchair mobility. In particular, these studies (1) develop and characterize a novel markerless motion capture-musculoskeletal model systems interface for kinematic assessment of manual wheelchair propulsion biomechanics, (2) conduct a longitudinal investigation of pediatric manual wheelchair users undergoing intensive community-based therapy to determine predictors of kinematic response, and (3) evaluate propulsion pattern-dependent training efficacy and musculoskeletal behavior using visual biofeedback.Results of the research studies show that taking a systems approach to the kinematic interface produces an effective and reliable system for kinematic assessment and training of manual wheelchair propulsion. The studies also show that the therapeutic outcomes and orthopaedic injury risk of pediatric manual wheelchair users are significantly related to the propulsion pattern employed. Further, these subjects can change their propulsion pattern in response to therapy even in the absence of wheelchair-based training, and have pattern-dependent differences in joint kinematics, musculotendon excursion, and training response. Further clinical research in this area is suggested, with a focus on refining physiotherapeutic training strategies for pediatric manual wheelchair users to develop safer and more effective propulsion patterns
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A machine learning approach for clinical gait analysis and classification of polymyalgia rheumatica using myoelectric sensors
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe study focuses on Polymyalgia Rheumatica (PMR), an autoimmune musculoskeletal disease primarily affecting the shoulder blade and hip muscles in older adults, particularly women aged 50 and above. The research aims to address two main challenges: the need for more clarity on the disease's pathophysiology and the challenge of identifying disease severity in patients. The study introduces a novel approach involving movement assessment, by designing a low-cost MyoTracker system, and using electromyography (EMG) features to understand the impact on patients' hip muscles. A clinical trial was conducted at Komfo Anokye Teaching Hospital in Ghana, where the study employed a qualitative research approach to monitor movement patterns. Participants were tasked to perform exercises comprising of gait, knee lifting, and knee extension with sensors attached to the hip muscles.
This research unfolds in three iterations, the first investigation involved hip muscular imbalances where the significant difference between patients and healthy controls in the maximum voluntary contraction (MVC) values was recorded. The bilateral difference computed between the left and right hip in patients exhibited 15% MVC on average compared to the healthy control group's 6%, indicating substantial hip muscular imbalances. The second iteration involved a movement assessment to identify specific movement patterns in patients. Support Vector Machine (SVM) achieves 85% accuracy for gait exercises, while Decision Tree (DT) performs less efficiently at 70%. SVM also excels in knee lifting exercises (70% accuracy), outperforming DT (60%). Based on hip muscle activation, patients' movement patterns significantly differ from healthy controls. In the third iteration, deep learning techniques, specifically RNN-LSTM and Vision Transformer (ViT), classify PMR disease severity based on EMG features. The study's results carry significant clinical implications with the evidence of hip muscular imbalances aiding in designing tailored rehabilitation protocols. Importantly, this study uses a cost-effective method for determining disease severity, enabling predictions about patients with severe PMR conditions. The key contribution of this thesis is the identification of patientsβ specific movement patterns and the determination of PMR severity among patients. Other contributions are the detection of hip muscular imbalance in patients and the design of rehabilitation protocols to address hip muscular imbalances and improve patients' range of motion, enhancing overall well-being. In conclusion, this comprehensive study leverages innovative approaches, from a MyoTracker system for movement assessment to deep learning models, to unravel the complexities of PMR disease. The collaboration with medical experts emphasises the potential real-world impact of this research in enhancing the treatment and recovery processes for individuals.Ghana Scholarship Secretaria
Π‘ΠΈΡΡΠ΅ΠΌ Π·Π° ΠΏΠΎΠ΄ΡΡΠΊΡ ΠΎΠ΄Π»ΡΡΠΈΠ²Π°ΡΡ, Π΅Π²Π°Π»ΡΠ°ΡΠΈΡΡ ΠΈ ΠΏΡΠ°ΡΠ΅ΡΠ΅ ΡΡΠ°ΡΠ° ΠΏΠ°ΡΠΈΡΠ΅Π½Π°ΡΠ° ΠΎΠ±ΠΎΠ»Π΅Π»ΠΈΡ ΠΎΠ΄ Π½Π΅ΡΡΠΎΠ΄Π΅Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠ²Π½ΠΈΡ Π±ΠΎΠ»Π΅ΡΡΠΈ
Π‘ΠΈΡΡΠ΅ΠΌΠΈ Π·Π° ΠΏΠΎΠ΄ΡΡΠΊΡ ΠΊΠ»ΠΈΠ½ΠΈΡΠΊΠΎΠΌ ΠΎΠ΄Π»ΡΡΠΈΠ²Π°ΡΡ ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΡΠ°ΡΡ ΡΠ°ΡΡΠ½Π°ΡΡΠΊΠ΅ Π°Π»Π°ΡΠ΅
ΠΊΠΎΡΠΈ ΠΏΡΠΈΠΌΠ΅Π½ΠΎΠΌ Π½Π°ΠΏΡΠ΅Π΄Π½ΠΈΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ° ΠΌΠΎΠ³Ρ ΡΡΠΈΡΠ°ΡΠΈ Π½Π° Π΄ΠΎΠ½ΠΎΡΠ΅ΡΠ΅ ΠΎΠ΄Π»ΡΠΊΠ° Ρ Π²Π΅Π·ΠΈ ΡΠ°
ΠΏΠ°ΡΠΈΡΠ΅Π½ΡΠΈΠΌΠ°. Π£ ΠΎΠ²ΠΎΡ Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠΈ ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΡΠ΅Π½ΠΈ ΡΡ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ΅ ΠΈ ΡΠ°Π·Π²ΠΎΡ Π½ΠΎΠ²ΠΎΠ³ ΡΠΈΡΡΠ΅ΠΌΠ° Π·Π°
ΠΏΠΎΠ΄ΡΡΠΊΡ ΠΎΠ΄Π»ΡΡΠΈΠ²Π°ΡΡ, Π΅Π²Π°Π»ΡΠ°ΡΠΈΡΡ ΠΈ ΠΏΡΠ°ΡΠ΅ΡΠ΅ ΡΡΠ°ΡΠ° ΠΏΠ°ΡΠΈΡΠ΅Π½Π°ΡΠ° ΠΎΠ±ΠΎΠ»Π΅Π»ΠΈΡ
ΠΎΠ΄
Π½Π΅ΡΡΠΎΠ΄Π΅Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠ²Π½ΠΈΡ
Π±ΠΎΠ»Π΅ΡΡΠΈ. ΠΠ½Π°Π»ΠΈΠ·Π° ΠΊΠ»ΠΈΠ½ΠΈΡΠΊΠΈ ΡΠ΅Π»Π΅Π²Π°Π½ΡΠ½ΠΈΡ
ΠΈ ΡΠ²Π°ΠΊΠΎΠ΄Π½Π΅Π²Π½ΠΈΡ
ΠΏΠΎΠΊΡΠ΅ΡΠ° ΡΠΈΠ½ΠΈ
ΠΎΡΠ½ΠΎΠ²Ρ ΠΎΠ²ΠΎΠ³ ΡΠΈΡΡΠ΅ΠΌΠ°. ΠΠ±ΡΠ°ΡΡΠΈ ΠΎΠ²ΠΈΡ
ΠΏΠΎΠΊΡΠ΅ΡΠ° ΡΠ½ΠΈΠΌΡΠ΅Π½ΠΈ ΡΡ ΠΏΠΎΠΌΠΎΡΡ Π±Π΅ΠΆΠΈΡΠ½ΠΈΡ
, Π½ΠΎΡΠΈΠ²ΠΈΡ
ΡΠ΅Π½Π·ΠΎΡΠ°
ΠΌΠ°Π»ΠΈΡ
Π΄ΠΈΠΌΠ΅Π½Π·ΠΈΡΠ° ΠΈ ΡΠ΅ΠΆΠΈΠ½Π΅, ΠΊΠΎΡΠΈ Π½Π΅ Π·Π°Ρ
ΡΠ΅Π²Π°ΡΡ ΠΊΠΎΠΌΠΏΠ»ΠΈΠΊΠΎΠ²Π°Π½Ρ ΠΏΠΎΡΡΠ°Π²ΠΊΡ ΠΈ ΠΌΠΎΠ³Ρ ΡΠ΅ ΡΠ΅Π΄Π½ΠΎΡΡΠ°Π²Π½ΠΎ
ΠΏΡΠΈΠΌΠ΅Π½ΠΈΡΠΈ Ρ Π±ΠΈΠ»ΠΎ ΠΊΠΎΠΌ ΠΎΠΊΡΡΠΆΠ΅ΡΡ. ΠΡΠ²ΠΈ Π΄Π΅ΠΎ ΡΠΈΡΡΠ΅ΠΌΠ° Π½Π°ΠΌΠ΅ΡΠ΅Π½ ΡΠ΅ (ΡΠ°Π½ΠΎΠΌ) ΠΏΡΠ΅ΠΏΠΎΠ·Π½Π°Π²Π°ΡΡ
ΠΠ°ΡΠΊΠΈΠ½ΡΠΎΠ½ΠΎΠ²Π΅ Π±ΠΎΠ»Π΅ΡΡΠΈ (ΠΠ) Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ Π°Π½Π°Π»ΠΈΠ·Π΅ Ρ
ΠΎΠ΄Π° ΠΈ Π°Π»Π³ΠΎΡΠΈΡΠ°ΠΌΠ° Π΄ΡΠ±ΠΎΠΊΠΎΠ³ ΡΡΠ΅ΡΠ°. Π Π΅Π·ΡΠ»ΡΠ°ΡΠΈ ΡΡ
ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ Π΄Π° ΡΠ΅ ΠΠ ΠΏΠ°ΡΠΈΡΠ΅Π½ΡΠ΅ ΠΌΠΎΠ³ΡΡΠ΅ ΠΏΡΠ΅ΠΏΠΎΠ·Π½Π°ΡΠΈ ΡΠ° Π²ΠΈΡΠΎΠΊΠΎΠΌ ΡΠ°ΡΠ½ΠΎΡΡΡ. ΠΡΡΠ³ΠΈ Π΄Π΅ΠΎ ΡΠΈΡΡΠ΅ΠΌΠ°
ΠΏΠΎΡΠ²Π΅ΡΠ΅Π½ ΡΠ΅ ΠΏΡΠ°ΡΠ΅ΡΡ ΡΠΈΠΌΠΏΡΠΎΠΌΠ° ΠΠ Π±ΡΠ°Π΄ΠΈΠΊΠΈΠ½Π΅Π·ΠΈΡΠ΅ ΠΏΡΠΈΠΌΠ΅Π½ΠΎΠΌ ΡΠ΅Π·ΠΎΠ½ΠΎΠ²Π°ΡΠ° ΠΊΠΎΡΠΈ ΡΠ΅ Π±Π°Π·ΠΈΡΠ° Π½Π°
Π·Π½Π°ΡΡ. ΠΡΠ΅Π΄ΡΡΠ°Π²ΡΠ΅Π½Π° ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Π° Π·Π° Π°Π½Π°Π»ΠΈΠ·Ρ ΠΏΠΎΠΊΡΠ΅ΡΠ° ΠΊΠΎΡΠΈ ΡΠ΅ ΠΊΠΎΡΠΈΡΡΠ΅ Π·Π° Π΅Π²Π°Π»ΡΠ°ΡΠΈΡΡ
Π±ΡΠ°Π΄ΠΈΠΊΠΈΠ½Π΅Π·ΠΈΡΠ΅. ΠΠΎΡΠ΅Π΄ ΡΠΎΠ³Π°, ΠΏΡΠΈΠΌΠ΅Π½ΠΎΠΌ ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΡ
ΠΌΠ΅ΡΠΎΠ΄Π° ΠΎΠ±ΡΠ°Π΄Π΅ ΡΠΈΠ³Π½Π°Π»Π° ΡΠ°Π·Π²ΠΈΡΠ΅Π½Π° ΡΠ΅ Π½ΠΎΠ²Π°
ΠΌΠ΅ΡΡΠΈΠΊΠ° Π·Π° ΠΊΠ²Π°Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΡ Π²Π°ΠΆΠ½ΠΈΡ
ΠΊΠ°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ° ΠΎΠ²ΠΈΡ
ΠΏΠΎΠΊΡΠ΅ΡΠ°. ΠΡΠ΅Π΄ΠΈΠΊΡΠΈΡΠ° ΡΡΠ΅ΠΏΠ΅Π½Π° ΡΠ°Π·Π²ΠΎΡΠ°
ΡΠΈΠΌΠΏΡΠΎΠΌΠ° ΡΠ΅ Π·Π°ΡΠ½ΠΈΠ²Π° Π½Π° Π½ΠΎΠ²ΠΎΠΌ Π΅ΠΊΡΠΏΠ΅ΡΡΡΠΊΠΎΠΌ ΡΠΈΡΡΠ΅ΠΌΡ ΠΊΠΎΡΠΈ Ρ ΠΏΠΎΡΠΏΡΠ½ΠΎΡΡΠΈ ΠΎΠ±ΡΠ΅ΠΊΡΠΈΠ²ΠΈΠ·ΡΡΠ΅ ΠΊΠ»ΠΈΠ½ΠΈΡΠΊΠ΅
Π΅Π²Π°Π»ΡΠ°ΡΠΈΠΎΠ½Π΅ ΠΊΡΠΈΡΠ΅ΡΠΈΡΡΠΌΠ΅. ΠΠ°Π»ΠΈΠ΄Π°ΡΠΈΡΠ° ΡΠ΅ ΡΡΠ°ΡΠ΅Π½Π° Π½Π° ΠΏΡΠΈΠΌΠ΅ΡΡ ΠΏΠΎΠΊΡΠ΅ΡΠ° ΡΠ°ΠΏΠΊΠ°ΡΠ° ΠΏΡΡΡΠΈΡΡ, ΠΊΠΎΡΠΈ ΡΠ΅
ΡΠ½ΠΈΠΌΡΠ΅Π½ Π½Π° ΠΏΠ°ΡΠΈΡΠ΅Π½Π°ΡΠΈΠΌΠ° ΡΠ° ΡΠΈΠΏΠΈΡΠ½ΠΈΠΌ ΠΈ Π°ΡΠΈΠΏΠΈΡΠ½ΠΈΠΌ ΠΏΠ°ΡΠΊΠΈΠ½ΡΠΎΠ½ΠΈΠ·ΠΈΠΌΠΎΠΌ. ΠΠΎΠΊΠ°Π·Π°Π½Π° ΡΠ΅ Π²ΠΈΡΠΎΠΊΠ°
ΡΡΠ°Π³Π»Π°ΡΠ΅Π½ΠΎΡΡ Ρ ΠΏΠΎΡΠ΅ΡΠ΅ΡΡ ΡΠ° ΠΊΠ»ΠΈΠ½ΠΈΡΠΊΠΈΠΌ ΠΏΠΎΠ΄Π°ΡΠΈΠΌΠ°. Π Π°Π·Π²ΠΈΡΠ΅Π½ΠΈ ΡΠΈΡΡΠ΅ΠΌ ΡΠ΅ ΠΎΠ±ΡΠ΅ΠΊΡΠΈΠ²Π°Π½,
Π°ΡΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½, ΡΠ΅Π΄Π½ΠΎΡΡΠ°Π²Π½ΠΎ ΡΠ΅ ΠΊΠΎΡΠΈΡΡΠΈ, ΡΠ°Π΄ΡΠΆΠΈ ΠΈΠ½ΡΡΠΈΡΠΈΠ²Π°Π½ Π³ΡΠ°ΡΠΈΡΠΊΠΈ ΠΈ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΠ°ΡΡΠΊΠΈ ΠΏΡΠΈΠΊΠ°Π·
ΡΠ΅Π·ΡΠ»ΡΠ°ΡΠ° ΠΈ Π·Π½Π°ΡΠ°ΡΠ½ΠΎ Π΄ΠΎΠΏΡΠΈΠ½ΠΎΡΠΈ ΡΠ½Π°ΠΏΡΠ΅ΡΠ΅ΡΡ ΠΊΠ»ΠΈΠ½ΠΈΡΠΊΠΈΡ
ΠΏΡΠΎΡΠ΅Π΄ΡΡΠ° Π·Π° Π΅Π²Π°Π»ΡΠ°ΡΠΈΡΡ ΠΈ ΠΏΡΠ°ΡΠ΅ΡΠ΅
ΡΡΠ°ΡΠ° ΠΏΠ°ΡΠΈΡΠ΅Π½Π°ΡΠ° ΡΠ° Π½Π΅ΡΡΠΎΠ΄Π΅Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠ²Π½ΠΈΠΌ Π±ΠΎΠ»Π΅ΡΡΠΈΠΌΠ°.Clinical decision support system represents a computer-aided tool that utilizes
advanced technologies for influencing clinical decisions about patients. This dissertation presents
research and development of a new decision support system for the assessment of patients with
neurodegenerative diseases. The analysis of movements that are part of standard clinical scales or
everyday activities represents the basis of the system. These movements are recorded using small and
lightweight wearable, wireless sensors, which do not require complicated setup and can be easily
applied in any environment. The first part of system is dedicated to the (early) recognition of
Parkinsonβs disease (PD) based on gait analysis and deep learning algorithms. PD patients could be
identified with a high accuracy. The other part of the system is dedicated to the assessment of PD
symptoms, more specifically, bradykinesia, utilizing the knowledge-based reasoning. A method for
analysis of bradykinesia related movements is defined and presented. Moreover, by applying different
signal processing techniques, new metrics have been developed to quantify the essential
characteristics of these movements. The prediction of symptom severity was performed using new
expert system that completely objectified the clinical evaluation criteria. Validation was performed
on the example of the finger-tapping movement of patients with typical and atypical parkinsonism.
A high compliance rate was obtained compared to clinical data. The developed system is objective,
automated, easy to use, contains an intuitive graphical and parametric presentation of results, and
significantly contributes to the improvement of clinical assessment of patients with
neurodegenerative diseases