Advanced multimodal visualisation of clinical gait and fluoroscopy analyses in the assessment of total knee replacement

Traditional gait and fluoroscopy analysis of human movement are largely utilised but are still limited in registration, integration, synchronisation and visualisation capabilities. The present work exploits the features of a recently developed software tool based on multimodal display (Data Manager developed within the EU-funded project 'Multimod') in an exemplary clinical case. Standard lower limb gait analysis, comprising segment position, ground reaction force and EMG data collection, and three-dimensional fluoroscopy analysis at the replaced joint were performed in a total knee replacement patient while ascending stairs. Clinical information such as X-rays and standard scores were also available. Data Manager was able to import all this variety of data and to structure these in an original hierarchical tree. Bone and prosthesis component models were registered to corresponding marker position data for effective three-dimensional animations. These were also synchronised with corresponding standard video sequences. Animations, video, time-histories of collected and also processed data were shown in various combinations, according to specific interests of the bioengineering and medical professionals expected to observe and to interpret this large amount of data. This software tool demonstrated to be a valuable means to enhance representation and interpretation of measurements coming from human motion analysis. In a single software, a thorough and effective clinical and biomechanical analysis of human motion was performed

Advanced multimodal visualisation of clinical gait and fluoroscopy analyses in the assessment of total knee replacement.

Traditional gait and fluoroscopy analysis of human movement are largely utilised but are still limited in registration, integration, synchronisation and visualisation capabilities. The present work exploits the features of a recently developed software tool based on multimodal display (Data Manager developed within the EU-funded project 'Multimod') in an exemplary clinical case. Standard lower limb gait analysis, comprising segment position, ground reaction force and EMG data collection, and three-dimensional fluoroscopy analysis at the replaced joint were performed in a total knee replacement patient while ascending stairs. Clinical information such as X-rays and standard scores were also available. Data Manager was able to import all this variety of data and to structure these in an original hierarchical tree. Bone and prosthesis component models were registered to corresponding marker position data for effective three-dimensional animations. These were also synchronised with corresponding standard video sequences. Animations, video, time-histories of collected and also processed data were shown in various combinations, according to specific interests of the bioengineering and medical professionals expected to observe and to interpret this large amount of data. This software tool demonstrated to be a valuable means to enhance representation and interpretation of measurements coming from human motion analysis. In a single software, a thorough and effective clinical and biomechanical analysis of human motion was performed

Metaheuristic Optimization Techniques for Articulated Human Tracking

Four adaptive metaheuristic optimization algorithms are proposed and demonstrated: Adaptive Parameter Particle Swarm Optimization (AP-PSO), Modified Artificial Bat (MAB), Differential Mutated Artificial Immune System (DM-AIS) and hybrid Particle Swarm Accelerated Artificial Immune System (PSO-AIS). The algorithms adapt their search parameters on the basis of the fitness of obtained solutions such that a good fitness value favors local search, while a poor fitness value favors global search. This efficient feedback of the solution quality, imparts excellent global and local search characteristic to the proposed algorithms. The algorithms are tested on the challenging Articulated Human Tracking (AHT) problem whose objective is to infer human pose, expressed in terms of joint angles, from a continuous video stream. The Particle Filter (PF) algorithms, widely applied in generative model based AHT, suffer from the 'curse of dimensionality' and 'degeneracy' challenges. The four proposed algorithms show stable performance throughout the course of numerical experiments. DM-AIS performs best among the proposed algorithms followed in order by PSO-AIS, AP-PSO, and MBA in terms of Most Appropriate Pose (MAP) tracking error. The MAP tracking error of the proposed algorithms is compared with four heuristic approaches: generic PF, Annealed Particle Filter (APF), Partitioned Sampled Annealed Particle Filter (PSAPF) and Hierarchical Particle Swarm Optimization (HPSO). They are found to outperform generic PF with a confidence level of 95%, PSAPF and HPSO with a confidence level of 85%. While DM-AIS and PSO-AIS outperform APF with a confidence level of 80%. Further, it is noted that the proposed algorithms outperform PSAPF and HPSO using a significantly lower number of function evaluations, 2500 versus 7200. The proposed algorithms demonstrate reduced particle requirements, hence improving computational efficiency and helping to alleviate the 'curse of dimensionality'. The adaptive nature of the algorithms is found to guide the whole swarm towards the optimal solution by sharing information and exploring a wider solution space which resolves the 'degeneracy' challenge. Furthermore, the decentralized structure of the algorithms renders them insensitive to accumulation of error and allows them to recover from catastrophic failures due to loss of image data, sudden change in motion pattern or discrete instances of algorithmic failure. The performance enhancements demonstrated by the proposed algorithms, attributed to their balanced local and global search capabilities, makes real-time AHT applications feasible. Finally, the utility of the proposed algorithms in low-dimensional system identification problems as well as high-dimensional AHT problems demonstrates their applicability in various problem domains

Contribution to the clinical validation of a generic method for the classification of osteoarthritic and non-pathological knee function

The Cardiff Dempster-Shafer (DS) classifier is a generic automated technique for analysing motion analysis (MA) data. It can accurately discriminate between level gait characteristics of non-pathological (NP) and osteoarthritic (OA) knee function. It can also quantify and visualise the functional outcome of a total knee replacement (TKR). A number of studies were undertaken to explore and enhance this method. The training set for the classifier was increased by 48% by collecting additional knee function data for level gait. Knee function for nine new patients was classified pre and post-TKR surgery. At 12 months post-TKR, two patients exhibited non-dominant NP knee function. The remaining patients did not recover NP gait. This finding is similar to previous classifications of level gait. To improve the distinction between varying degrees of knee function, stair gait was introduced into the trial. A staircase was designed and validated. Adduction and flexion moments acting about the knee joint and medial component of the ground reaction force were found to be important in the classification of OA and NP knee function from stair gait. Using a combination of these variables the DS classifier was able to characterise OA and NP function for 15 subjects correctly with 100% accuracy, determined using a leave-one-out method of cross validation. The variables were tested to assess the outcome of TKR surgery. The patient assessed recovered NP stair gait post surgery. An image based study was undertaken to investigate the quality of the MA data used in the DS classifier. A step up/down activity for 5 NP and 5 TKR subjects was recorded using non-simultaneous MA and dynamic fluoroscopy. Accurate knee kinematics were computed from the fluoroscopy images using KneeTrack image registration software. MA measured significantly larger knee joint translations and non-sagittal plane rotations. The largest errors in MA derived kinematics were 9.53 for adduction-abduction range of motion (ROM) measured from the NP cohort and 2.63cm compression-distraction ROM of the tibio-femoral joint, measured from the TKR cohort. The generic nature of the DS classifier was tested by its application to distinguish hip function following a lateral (LA) and posterior (PA) approach to total hip arthroplasty. The use of different variables was investigated with the classifier. The best classifier was able to distinguish between NP and LA function with 96.7% accuracy, LA and NP with 86.2% accuracy and between LA and PA with 81.5% accuracy. The PA approach was found to lead to more characteristic NP hip function than LA. These studies show that variables from stair gait should be included in addition to level gait in the classifier. Due to errors when measuring non-sagittal plane rotations using MA, these should be interpreted with caution. The generic nature of the classifier has been proven by its application to another joint, thus answering another orthopaedic question.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Contribution to the clinical validation of a generic method for the classification of osteoarthritic and non-pathological knee function

The Cardiff Dempster-Shafer (DS) classifier is a generic automated technique for analysing motion analysis (MA) data. It can accurately discriminate between level gait characteristics of non-pathological (NP) and osteoarthritic (OA) knee function. It can also quantify and visualise the functional outcome of a total knee replacement (TKR). A number of studies were undertaken to explore and enhance this method. The training set for the classifier was increased by 48% by collecting additional knee function data for level gait. Knee function for nine new patients was classified pre and post-TKR surgery. At 12 months post-TKR, two patients exhibited non-dominant NP knee function. The remaining patients did not recover NP gait. This finding is similar to previous classifications of level gait. To improve the distinction between varying degrees of knee function, stair gait was introduced into the trial. A staircase was designed and validated. Adduction and flexion moments acting about the knee joint and medial component of the ground reaction force were found to be important in the classification of OA and NP knee function from stair gait. Using a combination of these variables the DS classifier was able to characterise OA and NP function for 15 subjects correctly with 100% accuracy, determined using a leave-one-out method of cross validation. The variables were tested to assess the outcome of TKR surgery. The patient assessed recovered NP stair gait post surgery. An image based study was undertaken to investigate the quality of the MA data used in the DS classifier. A step up/down activity for 5 NP and 5 TKR subjects was recorded using non-simultaneous MA and dynamic fluoroscopy. Accurate knee kinematics were computed from the fluoroscopy images using KneeTrack image registration software. MA measured significantly larger knee joint translations and non-sagittal plane rotations. The largest errors in MA derived kinematics were 9.53 for adduction-abduction range of motion (ROM) measured from the NP cohort and 2.63cm compression-distraction ROM of the tibio-femoral joint, measured from the TKR cohort. The generic nature of the DS classifier was tested by its application to distinguish hip function following a lateral (LA) and posterior (PA) approach to total hip arthroplasty. The use of different variables was investigated with the classifier. The best classifier was able to distinguish between NP and LA function with 96.7% accuracy, LA and NP with 86.2% accuracy and between LA and PA with 81.5% accuracy. The PA approach was found to lead to more characteristic NP hip function than LA. These studies show that variables from stair gait should be included in addition to level gait in the classifier. Due to errors when measuring non-sagittal plane rotations using MA, these should be interpreted with caution. The generic nature of the classifier has been proven by its application to another joint, thus answering another orthopaedic question