Categorisation of activities of daily living of lower limb amputees during short-term use of a portable kinetic recording system: a preliminary study

The purpose of this preliminary study was to determine the relevance of the categorisation of the load regime data to assess the functional output and usage of the prosthesis of lower limb amputees. The objectives were (A) to introduce a categorisation of load regime, (B) to present some descriptors of each activity and (C) to report the results for a case. The load applied on the osseointegrated fixation of one transfemoral amputee was recorded using a portable kinetic system for five hours. The periods of directional locomotion, localised locomotion and stationary loading occurred 44%, 34% and 22% of recording time and each accounted for 51%, 38% and 12% of the duration of the periods of activity, respectively. The absolute maximum force during directional locomotion, localised locomotion and stationary loading was 19%, 15% and 8% of the BW on the antero-posterior axis, 20%, 19% and 12% on the medio-lateral axis as well as 121%, 106% and 99% on the long axis. A total of 2,783 gait cycles were recorded. Approximately 10% more gait cycles and 50% more of the total impulse than conventional analyses were identified. The proposed categorisation and apparatus have the potential to complement conventional instruments, particularly for difficult cases

Application and Evaluation of Lighthouse Technology for Precision Motion Capture

This thesis presents the development towards a system that can capture and quantify motion for applications in biomechanical and medical fields demanding precision motion tracking using the lighthouse technology. Commercially known as SteamVR tracking, the lighthouse technology is a motion tracking system developed for virtual reality applications that makes use of patterned infrared light sources to highlight trackers (objects embedded with photodiodes) to obtain their pose or spatial position and orientation. Current motion capture systems such as the camera-based motion capture are expensive and not readily available outside of research labs. This thesis provides a case for low-cost motion capture systems. The technology is applied to quantify motion to draw inferences about biomechanics capture and analysis, quantification of gait, and prosthetic alignment. Possible shortcomings for data acquisition using this system for the stated applications have been addressed. The repeatability of the system has been established by determining the standard deviation error for multiple trials based on a motion trajectory using a seven degree-of-freedom robot arm. The accuracy testing for the system is based on cross-validation between the lighthouse technology data and transformations derived using joint angles by developing a forward kinematics model for the robot’s end-effector pose. The underlying principle for motion capture using this system is that multiple trackers placed on limb segments allow to record the position and orientation of the segments in relation to a set global frame. Joint angles between the segments can then be calculated from the recorded positions and orientations of each tracker using inverse kinematics. In this work, inverse kinematics for rigid bodies was based on calculating homogeneous transforms to the individual trackers in the model’s reference frame to find the respective Euler angles as well as using the analytical approach to solve for joint variables in terms of known geometric parameters. This work was carried out on a phantom prosthetic limb. A custom application-specific motion tracker was also developed using a hardware development kit which would be further optimized for subsequent studies involving biomechanics motion capture

The Design, Development, and Validation of a Residual Limb Evaluation System for the Real-Time Data Mapping of the Trans-Tibial Amputee Socket-Limb Interface for Prosthetic Fitment

Introduction: Skin problems are known to occur on the residual limb (RL) of trans-tibial amputees (TTAs). These are induced by an improper prosthetic socket fitment, alignment or component selection. It was identified that there is a lack of RL evaluation systems (RLESs) that are tailored for the prosthetic fitting procedure that analyse the pressures and temperatures on the RL, as well as the gait phases of stance. This observation established the hypothesis that a tactile RL evaluation device and recording software system can provide reliable socket-limb interface (SLI) information which can be used to identify vulnerable areas on the RL induced by the socket during the gait movements of TTAs. Methods: A prototype RLES was designed and developed. It was comprised of tactile pressure and temperature transducers, gait ground reaction force (GRF) transducers and device-specific software tailored for the evaluation of the RL within the SLI. A pilot study was designed to evaluate the capabilities of the RLES which entailed the evaluation of its skin temperature tracking ability, pressure measurement repeatability within the SLI, and ability to interpret the pressures during (natural) walking movements. Study participants were recruited through the private practice of prosthetist Eugene Russouw, as well as Vincent Palloti Hospital (South Africa, Cape Town) and consisted of two bilateral and three unilateral TTAs, who were enrolled in the pilot study. Each participant performed three experimental procedures: a static stand (SS); a straight-line walk (SLW); and a figure-of-8 walk (F8W). Skin temperature change due to loading and unloading was monitored during the SS procedure. Peak pressure results from the SLW and F8W procedures were gathered to evaluate the coefficient of variance (COV) between strides. This was used to evaluate the repeatability of the pressure measurements and allowed for a comparison between the SLW and F8W methods. GRF data collected from the SLW dataset was used to evaluate the RLES's ability to track gait phases. Results: The developed RLES software provided a tailored prosthetic fitting analysis platform (in the form of a graphical user interface) which allowed the user to perform a real-time, in-depth analysis of different RL areas, as well as provided an overview of all areas simultaneously. It provided functions for the recording, playback, and export of testing data which was used to evaluate the RLES capabilities. The RLES produced an average COV of 7.16%, which fell within the 6.94% ± 1.7% range in literature. The SS procedure produced an average temperature increase of 0.45 °C, found over all RL areas, which corresponds to similar studies in literature. This validated its ability to track RL skin temperature by producing the expected skin temperature change trend. Additionally, the RLES produced an expected TTA gait GRF curve (similar to literature) in which different gait phases could be identified. The comparison between the SLW and F8W methods found that pressure sore areas endured large pressures without relief from other movements (when compared to healthier areas), and suggests that the SLW and F8W comparison may be an important additional evaluation method during the prosthetic fitting procedure. The RLES identified all of the pressure sores presented within the 24 RL areas over all the TTA participants and suggested that a safe pressure threshold of 100 kPa is an appropriate guideline to be used during the prosthetic fitting procedure. Conclusions: The RLES proved to work efficiently and successfully within the study, and was capable of identifying vulnerable areas of pressure sores. With the high prevalence of skin problems on the RLs of TTAs, the implementation of a RLES during the fitting procedure, which can tailor the prosthesis design and fitment to the amputee, may potentially identify vulnerable areas of future skin problems and allow preventative actions to be performed

Real-Time Fall Detection and Response on Mobile Phones Using Machine Learning

Falls are common and often dangerous for groups with impaired mobility, like the elderly or people with lower limb amputations. Finding ways of minimizing the frequency or impact of a fall can improve quality of life dramatically. When someone does fall, real-time detection of the fall and a long-lie can trigger fast medical assistance. Such a system can also collect reliable data on the nature of real-world falls that can be used to better understand the circumstances, to aid in prevention efforts. This work has been to develop a real-time fall tracking system specifically for subjects with lower limb amputations. In this study 17 subjects (10 healthy controls and 7 amputees) were asked to simulate 4 types of falls (trip, slip, right and left lateral) 3 times each with a mobile phone placed at 3 different locations on the body (pouch, pocket, and hand). Signals were collected from the accelerometer, gyroscope and barometer sensors using the Android mobile phone application Purple Robot. We compared 5 different machine learning classifiers for fall detection: logistic regression (L1 and L2 norm), support vector machines, K-nearest neighbors, decision trees, and random forest. Logistic regression (L1 regularized lasso ) and random forest yielded the best results on the test set (98.8% and 98.4%, respectively). There was no significant difference between amputee and healthy control falls in terms of classifier accuracy. When testing on real world data with no recorded falls, the false positive rate was only 0.07%. In addition to offline algorithmic development, the detection system was implemented for real-time application on a mobile platform. The previously-trained logistic regression model was implemented on the mobile platform for real-time detection. This platform will be used in an upcoming amputee population falls study. The completed system will gather data on the current conditions leading to the fall (weather, GPS location, etc.) and classify the type of the fall. The system will follow up with notifications requesting a response from the user, or automatically notify emergency contacts or 911 as needed. The steps taken in creating this system bring us closer to real-time intervention strategies to minimize the impact of falls, and enable us to collect accurate falls-related data to improve fall prevention strategies and prosthesis design

A Biomimetic Approach to Controlling Restorative Robotics

Movement is the only way a person can interact with the world around them. When trauma to the neuromuscular systems disrupts the control of movement, quality of life suffers. To restore limb functionality, active robotic interventions and/or rehabilitation are required. Unfortunately, the primary obstacle in a person’s recovery is the limited robustness of the human-machine interfaces. Current systems rely on control approaches that rely on the person to learn how the system works instead of the system being more intuitive and working with the person naturally. My research goal is to design intuitive control mechanisms based on biological processes termed the biomimetic approach. I have applied this control scheme to problems with restorative robotics focused on the upper and lower limb control. Operating an advanced active prosthetic hand is a two-pronged problem of actuating a high-dimensional mechanism and controlling it with an intuitive interface. Our approach attempts to solve these problems by going from muscle activity, electromyography (EMG), to limb kinematics calculated through dynamic simulation of a musculoskeletal model. This control is more intuitive to the user because they attempt to move their hand naturally, and the prosthetic hand performs that movement. The key to this approach was validating simulated muscle paths using both experimental measurements and anatomical constraints where data is missing. After the validation, simulated muscle paths and forces are used to decipher the intended movement. After we have calculated the intended movement, we can move a prosthetic hand to match. This approach required minimal training to give an amputee the ability to control prosthetic hand movements, such as grasping. A more intuitive controller has the potential to improve how people interact and use their prosthetic hands. Similarly, the rehabilitation of the locomotor system in people with damaged motor pathways or missing limbs require appropriate interventions. The problem of decoding human motor intent in a treadmill walking task can be solved with a biomimetic approach. Estimated limb speed is essential for this approach according to the theoretical input-output computation performed by spinal central pattern generators (CPGs), which represents neural circuitry responsible for autonomous control of stepping. The system used the locomotor phases, swing and stance, to estimate leg speeds and enable self-paced walking as well as steering in virtual reality with congruent visual flow. The unique advantage of this system over the previous state-of-art is the independent leg speed control, which is required for multidirectional movement in VR. This system has the potential to contribute to VR gait rehab techniques. Creating biologically-inspired controllers has the potential to improve restorative robotics and allow people a better opportunity to recover lost functionality post-injury. Movement is the only way a person can interact with the world around them. When trauma to the neuromuscular systems disrupts the control of movement, quality of life suffers. To restore limb functionality, active robotic interventions and/or rehabilitation are required. Unfortunately, the primary obstacle in a person’s recovery is the limited robustness of the human-machine interfaces. Current systems rely on control approaches that rely on the person to learn how the system works instead of the system being more intuitive and working with the person naturally. My research goal is to design intuitive control mechanisms based on biological processes termed the biomimetic approach. I have applied this control scheme to problems with restorative robotics focused on the upper and lower limb control.Operating an advanced active prosthetic hand is a two-pronged problem of actuating a high-dimensional mechanism and controlling it with an intuitive interface. Our approach attempts to solve these problems by going from muscle activity, electromyography (EMG), to limb kinematics calculated through dynamic simulation of a musculoskeletal model. This control is more intuitive to the user because they attempt to move their hand naturally, and the prosthetic hand performs that movement. The key to this approach was validating simulated muscle paths using both experimental measurements and anatomical constraints where data is missing. After the validation, simulated muscle paths and forces are used to decipher the intended movement. After we have calculated the intended movement, we can move a prosthetic hand to match. This approach required minimal training to give an amputee the ability to control prosthetic hand movements, such as grasping. A more intuitive controller has the potential to improve how people interact and use their prosthetic hands.Similarly, the rehabilitation of the locomotor system in people with damaged motor pathways or missing limbs require appropriate interventions. The problem of decoding human motor intent in a treadmill walking task can be solved with a biomimetic approach. Estimated limb speed is essential for this approach according to the theoretical input-output computation performed by spinal central pattern generators (CPGs), which represents neural circuitry responsible for autonomous control of stepping. The system used the locomotor phases, swing and stance, to estimate leg speeds and enable self-paced walking as well as steering in virtual reality with congruent visual flow. The unique advantage of this system over the previous state-of-art is the independent leg speed control, which is required for multidirectional movement in VR. This system has the potential to contribute to VR gait rehab techniques.Creating biologically-inspired controllers has the potential to improve restorative robotics and allow people a better opportunity to recover lost functionality post-injury

Novel Bidirectional Body - Machine Interface to Control Upper Limb Prosthesis

Objective. The journey of a bionic prosthetic user is characterized by the opportunities and limitations involved in adopting a device (the prosthesis) that should enable activities of daily living (ADL). Within this context, experiencing a bionic hand as a functional (and, possibly, embodied) limb constitutes the premise for mitigating the risk of its abandonment through the continuous use of the device. To achieve such a result, different aspects must be considered for making the artificial limb an effective support for carrying out ADLs. Among them, intuitive and robust control is fundamental to improving amputees’ quality of life using upper limb prostheses. Still, as artificial proprioception is essential to perceive the prosthesis movement without constant visual attention, a good control framework may not be enough to restore practical functionality to the limb. To overcome this, bidirectional communication between the user and the prosthesis has been recently introduced and is a requirement of utmost importance in developing prosthetic hands. Indeed, closing the control loop between the user and a prosthesis by providing artificial sensory feedback is a fundamental step towards the complete restoration of the lost sensory-motor functions. Within my PhD work, I proposed the development of a more controllable and sensitive human-like hand prosthesis, i.e., the Hannes prosthetic hand, to improve its usability and effectiveness. Approach. To achieve the objectives of this thesis work, I developed a modular and scalable software and firmware architecture to control the Hannes prosthetic multi-Degree of Freedom (DoF) system and to fit all users’ needs (hand aperture, wrist rotation, and wrist flexion in different combinations). On top of this, I developed several Pattern Recognition (PR) algorithms to translate electromyographic (EMG) activity into complex movements. However, stability and repeatability were still unmet requirements in multi-DoF upper limb systems; hence, I started by investigating different strategies to produce a more robust control. To do this, EMG signals were collected from trans-radial amputees using an array of up to six sensors placed over the skin. Secondly, I developed a vibrotactile system to implement haptic feedback to restore proprioception and create a bidirectional connection between the user and the prosthesis. Similarly, I implemented an object stiffness detection to restore tactile sensation able to connect the user with the external word. This closed-loop control between EMG and vibration feedback is essential to implementing a Bidirectional Body - Machine Interface to impact amputees’ daily life strongly. For each of these three activities: (i) implementation of robust pattern recognition control algorithms, (ii) restoration of proprioception, and (iii) restoration of the feeling of the grasped object's stiffness, I performed a study where data from healthy subjects and amputees was collected, in order to demonstrate the efficacy and usability of my implementations. In each study, I evaluated both the algorithms and the subjects’ ability to use the prosthesis by means of the F1Score parameter (offline) and the Target Achievement Control test-TAC (online). With this test, I analyzed the error rate, path efficiency, and time efficiency in completing different tasks. Main results. Among the several tested methods for Pattern Recognition, the Non-Linear Logistic Regression (NLR) resulted to be the best algorithm in terms of F1Score (99%, robustness), whereas the minimum number of electrodes needed for its functioning was determined to be 4 in the conducted offline analyses. Further, I demonstrated that its low computational burden allowed its implementation and integration on a microcontroller running at a sampling frequency of 300Hz (efficiency). Finally, the online implementation allowed the subject to simultaneously control the Hannes prosthesis DoFs, in a bioinspired and human-like way. In addition, I performed further tests with the same NLR-based control by endowing it with closed-loop proprioceptive feedback. In this scenario, the results achieved during the TAC test obtained an error rate of 15% and a path efficiency of 60% in experiments where no sources of information were available (no visual and no audio feedback). Such results demonstrated an improvement in the controllability of the system with an impact on user experience. Significance. The obtained results confirmed the hypothesis of improving robustness and efficiency of a prosthetic control thanks to of the implemented closed-loop approach. The bidirectional communication between the user and the prosthesis is capable to restore the loss of sensory functionality, with promising implications on direct translation in the clinical practice

Socket interface pressure and amputee reported outcomes for comfortable and uncomfortable conditions of patellar tendon bearing socket: a pilot study

The objectives of the current study were to compare intra-socket pressure differences between comfortable and uncomfortable socket conditions, and the usefulness of subject perception of satisfaction, activity limitations, and socket comfort in distinguishing between these two socket conditions. Five unilateral trans-tibial amputees took part in the study. They answered the Socket Comfort Score (SCS) and Trinity Amputation and Prosthetic Experience Scale (TAPES) questionnaires before the interface pressure (in standing and walking) was measured for the uncomfortable socket condition at five regions of the residual limb. Participants were then provided with a comfortable socket and wore it for two weeks. Participants who were satisfied with the socket fit after two weeks repeated the SCS and TAPES questionnaires and interface pressure measurements. The differences between the test results of the two conditions were not statistically significant, except for the interface pressure at the popliteal region during the early stance phase, TAPES socket fit subscale, and the SCS. Due to large variability of the data and the lack of statistical significance, no firm conclusion can be made on the possible relationship between the interface pressure values and the patient-reported outcomes of the two socket conditions. A larger sample size and longer acclimation period are required to locate significant differences.N/