Ideal prostheses are defined as artificial limbs that would permit physically impaired individuals freedom of movement and independence rather than a life of disability and dependence. Current lower limb prostheses range from a single mechanical revolute joint to advanced microprocessor controlled mechanisms. Despite the advancement in technology and medicine, current lower limb prostheses are still lacking an actuation element, which prohibits patients from regaining their original mobility and improving their quality of life. This thesis aims to design and test a Pneumatic Artificial Muscle that would actuate lower limb prostheses. This would offer patients the ability to ascend and descend stairs as well as standing up from a sitting position. A comprehensive study of knee biomechanics is first accomplished to characterize the actuation requirement, and subsequently a Pneumatic Artificial Muscle design is proposed. A novel design of muscle end fixtures is presented which would allow the muscle to operate at a gage pressure surpassing 2.76 MPa (i.e. 400 psi) and yield a muscle force that is at least 3 times greater than that produced by any existing equivalent Pneumatic Artificial Muscle. Finally, the proposed Pneumatic Artificial Muscle is tested and validated to verify that it meets the size, weight, kinetic and kinematic requirements of human knee articulation.

Ideal prostheses are defined as artificial limbs that would permit physically impaired individuals freedom of movement and independence rather than a life of disability and dependence. Current lower limb prostheses range from a single mechanical revolute joint to advanced microprocessor controlled mechanisms. Despite the advancement in technology and medicine, current lower limb prostheses are still lacking an actuation element, which prohibits patients from regaining their original mobility and improving their quality of life. This thesis aims to design and test a Pneumatic Artificial Muscle that would actuate lower limb prostheses. This would offer patients the ability to ascend and descend stairs as well as standing up from a sitting position. A comprehensive study of knee biomechanics is first accomplished to characterize the actuation requirement, and subsequently a Pneumatic Artificial Muscle design is proposed. A novel design of muscle end fixtures is presented which would allow the muscle to operate at a gage pressure surpassing 2.76 MPa (i.e. 400 psi) and yield a muscle force that is at least 3 times greater than that produced by any existing equivalent Pneumatic Artificial Muscle. Finally, the proposed Pneumatic Artificial Muscle is tested and validated to verify that it meets the size, weight, kinetic and kinematic requirements of human knee articulation.

Measuring lower limb strength and symmetry is a common practice in elite sports to determine the return to sports point in time during rehabilitation, and this information could be useful for amateur athletes too. However, the devices used for this are highly sophisticated and hardly accessible. The aim of this project is to devise an affordable system for lower limb strength measurement which can be integrated with a common training system, to make this type of information widely available. The system is built up on a flywheel-based leg extension machine (LegExx by Exxentric AB) that allows quadriceps training at maximal force in concentric and eccentric contraction. Inexpensive standard components and software were used for prototyping. The parameters of interest were angular velocity of extension/flexion in the knee and force applied by the athlete. In the first step, evaluation was limited to the concentric phase of movement. Angular velocity and leg position was measured with a gyroscope sensor (Movesense by Suunto). For force measurement, two approaches were tested: In prototype A, the tension of the drive belt, which ultimately puts the flywheel into rotation, was measured with a sensor for tensile forces (Tindeq Progressor 300). In prototype B, a more direct measurement was used, with force sensors mounted under the contact point of the athlete’s shank with the swing beam of the LegExx. Sensor data were transmitted via Bluetooth to a mobile device or a laptop and displayed graphically after synchronization of the data streams. Force values were converted to torque using inverse kinematics to make the two prototypes comparable. The first prototypes were shown to be workable and yielded similar readings for concentric peak torque, with realistic wave forms in the graphical display. Comparison with results of a gold standard isokinetic dynamometer revealed, however, major discrepancies mainly regarding the absolute torque values. Oscillation in the belt system were identified as an issue in prototype A, while cross talk between sensors and vulnerability to leg placement occurred in prototype B. However, these issues are not insurmountable, and it is suggested to proceed with the development of prototype B, as it has the advantage of simultaneous measurement and direct comparison of both legs. / Att mäta styrka och symmetri i nedre extremiteterna är vanligt inom elitidrott för att avgöra när man kan återgå till idrottsutövning under rehabiliteringen, och denna information kan vara användbar även för amatöridrottare. De apparater som används för detta är dock mycket sofistikerade och svårtillgängliga. Syftet med detta projekt är att utforma ett prisvärt system för mätning av styrkan i nedre extremiteterna som kan integreras med ett vanligt träningssystem, för att göra denna typ av information allmänt tillgänglig. Systemet är uppbyggt på en svänghjulsbaserad benspark maskin (LegExx från Exxentric AB) som möjliggör quadriceps träning vid maximal kraft i koncentrisk och excentrisk kontraktion. Kostnadseffektiva standardkomponenter och mjukvara användes för prototypframställning. De parametrar som var av intresse var vinkelhastighet för utsträckning- /böjning i knäet och den kraft som utövades av idrottaren. I det första steget begränsades utvärderingen till rörelsens koncentriska fas. Vinkelhastighet och benposition mättes med en gyroskopisk sensor (Movesense från Suunto). För kraftmätning testades två tillvägagångssätt: I prototyp A mättes dragkraften i drivremmen, som i slutändan sätter svänghjulet i rotation, med en sensor för dragkrafter (Tindeq Progressor 300). I prototyp B användes en mer direkt mätning med kraftsensorer som monterades under kontaktpunkten mellan idrottsutövarens smalben och LegExx:s svängbalk. Sensordata överfördes via Bluetooth till en mobil enhet eller en bärbar dator och visades grafiskt efter synkronisering av dataströmmarna. Kraftvärden omvandlades till vridmoment med hjälp av invers kinematik för att göra de två prototyperna jämförbara. De första prototyperna visade sig fungera och gav liknande mätningar för koncentriskt toppmoment, med realistiska vågformer i den grafiska displayen. En jämförelse med resultaten från en isokinetisk dynamometer med guldstandard visade dock på stora skillnader, främst när det gäller de absoluta vridmomentvärdena. Oscillation i bältesystemet identifierades som ett problem i prototyp A, medan överkoppling mellan sensorer och känslighet för benplacering förekom i prototyp B. Dessa problem är dock inte oöverstigliga, och det föreslås att man fortsätter att utveckla prototyp B, eftersom den har fördelen av samtidig mätning och direkt jämförelse av båda benen.

Global rise in life expectancy has resulted in an increased demand for affordable healthcare and monitoring services. The advent of miniature and low–power sensor technologies coupled with the emergence of the Internet–of–Things has paved the way towards affordable health monitoring tools in wearable platforms. However, ensuring power–efficient operation, data accuracy and user comfort are critical for such wearable systems. This thesis focuses on the development of accurate and computationally efficient algorithms and low–cost, unobtrusive devices with potential predictive capability for monitoring mobility and cardiac health in a wearable platform. A three–stage complementary filter–based approach is developed to realize a computationally efficient method to estimate sensor orientation in real–time. A gradient descent–based approach is used to estimate the gyroscope integration drift, which is subsequently subtracted from the integrated gyroscope data to get the sensor orientation. This predominantly gyroscope–based orientation estimation approach is least affected by external acceleration and magnetic disturbances. A two–stage complementary filter–based efficient sensor fusion algorithm is developed for real–time monitoring of lower–limb joints that estimates the IMU inclinations in the first stage and uses a gradient descent–based approach in the second stage to estimate the joint angles. The proposed method estimates joint angles primarily from the gyroscope measurements without incorporating the magnetic field measurement, rendering the estimated angles least affected by any external acceleration and insensitive to magnetic disturbances. An IMU–based simple, low–cost and computationally efficient gait–analyzer is developed to track the course of an individual's gait health in a continuous fashion. Continuous monitoring of gait patterns can potentially enable detecting musculoskeletal or neurodegenerative diseases at the early onset. The proposed gait analyzer identifies an anomalous gait with moderate to high accuracy by evaluating the gait features with respect to the baseline clusters corresponding to an individual’s healthy peer group. The adoption of a computationally efficient signal analysis technique renders the analyzer suitable for systems with limited processing capabilities. A flexible dry capacitive electrode and a wireless ECG monitoring system with automatic anomaly detection capability are developed. The flexible capacitive electrode reduces motion artifacts and enables sensing bio–potential over a dielectric material such as cotton cloth. The virtual ground of the electrode allows for obtaining single–lead ECG using two electrodes only. ECG measurements obtained over different types of textile materials and in presence of body movements show comparable performance to other reported ECG monitoring systems. An algorithm is developed separately as a potential extension of the software to realize automatic identification of Atrial Fibrillation from short single–lead ECGs. The association between human gait and cardiac activities is studied. The gait is measured using wearable IMUs and the cardiac activity is measured with a single–lead handheld ECG monitor. Some key cardiac parameters, such as heart rate and heart rate variability and physical parameters, such as age and BMI show good association with gait asymmetry and gait variation. These associations between gait and heart can be useful in realizing low–cost in–home personal monitoring tool for early detecting CVD–related changes in gait features before the CVD symptoms are manifested. / Thesis / Doctor of Philosophy (PhD) / Wearable health monitoring systems can be a viable solution to meet the increased demand for affordable healthcare and monitoring services. However, such systems need to be energy–efficient, accurate and ergonomic to enable long–term monitoring of health reliably while preserving user comfort. In this thesis, we develop efficient algorithms to obtain real–time estimates of on–body sensors' orientation, gait parameters such as stride length, and gait velocity and lower–limb joint angles. Furthermore, we develop a simple, low–cost and computationally efficient gait–analyzer using miniature and low–power inertial motion units to track the health of human gait in a continuous fashion. In addition, we design flexible, dry capacitive electrodes and use them to develop a portable single–lead electrocardiogram (ECG) device. The flexible design ensures better conformity of the electrode to the skin, resulting in better signal quality. The capacitive nature allows for obtaining ECG signals over insulating materials such as cloth, thereby potentially enabling a comfortable means of long–term cardiac health monitoring at home. Besides, we implement an automatic anomaly detection algorithm that detects Atrial Fibrillation with good accuracy from short single–lead ECGs. Finally, we investigate the association between gait and cardiac activities. We observe that some important cardiac signs, such as heart rate and heart rate variability and physical parameters, such as age and BMI show good association with gait asymmetry and gait variation.

Lower-limb exoskeletons are an emerging technology to provide walking assistance to people who have a spinal cord injury (SCI). Until now, exoskeletons have primarily been used in a clinical setting for a range of applications in rehabilitation, and there is potential for exoskeletons to be used by people with SCI at home. Daily walking with an exoskeleton contributes significantly to physical and mental health of the user, but previous work has concluded that further development is required before exoskeletons are broadly adopted for this purpose. ABLE Human Motion is currently working to create a lightweight and intuitive exoskeleton for home use. To understand how this exoskeleton must be designed differently from clinical rehabilitation exoskeletons, it is necessary to understand the user requirements of the device in depth. This thesis explored: 1) what methodology is appropriate for evaluating home use exoskeletons, 2) what users want to use a personal exoskeleton for, and 3) what design changes distinguish an exoskeleton for home use instead of rehabilitation. This was done using a combination of literature review, hazard analysis, user observations (n=7), user interviews (n=7), and physiotherapist interviews (n=3) to derive a detailed set of user requirements and product specifications for a personal exoskeleton for home use. Interviews were conducted face-to-face and analyzed using thematic analysis. Results of the study show that users primarily want to use a personal exoskeleton for daily exercise and wellness activities, in outdoor environments, and around the theme of “like-everyone-else”. Therapists added an additional theme of user trust in the device. These insights have been translated into a set of prioritized user requirements and product specifications for a lower-limb exoskeleton for walking assistance after SCI, which can be used in the future design and development of such a device. Future work will be to develop testing setups to further explore the product specifications, and to conduct observation studies of the exoskeleton being used in a home-like environment. / Exoskelett för de nedre extremiteterna är en framväxande teknik för att ge gångassistans till personer som har en ryggmärgsskada. Hittills har exoskelett främst använts i en klinisk miljö för en rad tillämpningar inom rehabilitering, men det finns potential för exoskelett att användas av personer med ryggmärgsskada för personligt bruk i hemmet. För att förstå hur personliga exoskelett måste utformas annorlunda än kliniska exoskelett är det nödvändigt att på djupet förstå användarens krav på enheten. Detta projekt använde en kombination av litteraturgranskning, riskanalys, användarobservationer, användarintervjuer och fysioterapeutintervjuer för att härleda en detaljerad uppsättning användarkrav och produktspecifikationer för ett personligt exoskelett för hemmabruk. Intervjuer analyserades med hjälp av tematisk analys. Resultaten av studien visar att användarna i första hand vill använda ett personligt exoskelett för dagliga tränings- och hälsoaktiviteter, i utomhusmiljöer och på temat ”som alla andra”. Andra viktiga teman för framtida utveckling var kring användarnas förtroende för enheten och bibehållande av motivation för daglig träning. Dessa teman har översatts till en uppsättning prioriterade användarkrav och produktspecifikationer för ett nedre extremitetsskelett för gångassistans efter en ryggmärgsskada som kan användas i framtida design och utveckling av en sådan enhet.

Dans cette thèse, nous présentons une méthode de segmentation d’images radiographiques des membres inférieurs en régions d’intérêt (ROIs), une méthode de recalage rigide tridimensionnel (3D) / bidimensionnel (2D) des prothèses du genou sur les deux images biplanaires radiographiques calibrées et une méthode de reconstruction 3D des membres inférieurs à partir de deux images biplanaires radiographiques calibrées. Le premier article présente une méthode de segmentation de rotule, astragale et bassin des images radiographiques en régions d’intérêt basée sur la fusion de multi-atlas et superpixels. Cette méthode utilise l’apprentissage d’une base de données d’images radiographiques de ces os segmentées manuellement et recalées entre elles pour estimer un ensemble de superpixels permettant de tenir compte de toute la variabilité locale et non linéaire existante dans la base, puis la propagation d’étiquettes basée sur le concept d’entropie pour raffiner la carte de segmentations en régions internes afin d’obtenir le résultat final. Le deuxième article présente une méthode de recalage rigide 3D / 2D des composants tibiaux et fémoraux de prothèse du genou sur deux images biplanaires radiographiques calibrées. Cette méthode utilise une mesure de similarité hybride basée sur les notions de contours et régions puis un algorithme d’optimisation stochastique pour estimer la position des composants. La similarité basée sur les régions est stable et robuste contre les bruits. Cependant, cette mesure n’est pas précise car le nombre de pixels aux contours est inférieur au celui à l’intérieur de la région. Au contraire, la similarité basée sur les contours est précise mais plus sensible au bruit ou à d’autres artefacts existant dans les images. C’est pourquoi la combinaison de ces deux similarités fournit une méthode de recalage robuste et précise. Le troisième article représente une méthode statistique biplanaire de reconstruction 3D de rotule, astragale et bassin. Cette méthode utilise un algorithme de réduction de dimensionnalité pour définir un modèle déformable paramétrique qui contient toutes les déformations statistiques admissibles apprises à partir d’une base de données des structures osseuses. Puis un algorithme d’optimisation stochastique est utilisé pour minimiser la différence entre la projection des contours / régions des modèles surfaciques osseux avec ceux segmentés sur les deux images radiographiques. / In this thesis, we present a segmentation method of lower limbs of X-ray images into regions of interest (ROIs), a three-dimensional (3D) / two-dimensional (2D) rigid registration method of knee implant components to biplanar X-ray images, and a 3D reconstruction method of the lower limbs using biplanar X-ray images. The first paper presents a superpixel and multi-atlas-based segmentation method of the patella, talus, and pelvis into regions of interest. This method uses a training dataset of pre-segmented and co-registered X-ray images of these bones to estimate a collection of superpixels allowing to take into account all the nonlinear and local variability existing in the dataset, then a propagation of label based on the entropy concept for refining the segmentation map into internal regions to the final result. The second paper presents a 3D / 2D rigid registration method of tibial and femoral components of knee implants to calibrated biplanar X-ray images. This method uses a hybrid edge- and region-based similarity measure then a stochastic optimization algorithm to estimate the component position. The region-based similarity is stable and robust to noise. However, this measure is not precise because the number of pixels in the border is fewer than the number of pixels inside the region. On the contrary, the edge-based similarity is accurate but more sensitive to noise or other artifacts existing in the images. That’s why the combination of these two similarity types provides a robust and accurate registration method. The third paper presents a statistical biplanar 3D reconstruction method of the patella, talus, and pelvis. This method uses a dimensionality reduction algorithm to define a deformable parametric model which contains all admissible statistical deformations learned from the bone structure dataset. Then a stochastic optimization algorithm is used to minimize the difference between the contour / region projection of bone models and the contours / regions in two segmented X-ray images.