Human Feedback in Statistical Machine Translation

The thesis addresses the challenge of improving Statistical Machine Translation (SMT) systems via feedback given by humans on translation quality. The amount of human feedback available to systems is inherently low due to cost and time limitations. One of our goals is to simulate such information by automatically generating pseudo-human feedback. This is performed using Quality Estimation (QE) models. QE is a technique for predicting the quality of automatic translations without comparing them to oracle (human) translations, traditionally at the sentence or word levels. QE models are trained on a small collection of automatic translations manually labelled for quality, and then can predict the quality of any number of unseen translations. We propose a number of improvements for QE models in order to increase the reliability of pseudo-human feedback. These include strategies to artificially generate instances for settings where QE training data is scarce. We also introduce a new level of granularity for QE: the level of phrases. This level aims to improve the quality of QE predictions by better modelling inter-dependencies among errors at word level, and in ways that are tailored to phrase-based SMT, where the basic unit of translation is a phrase. This can thus facilitate work on incorporating human feedback during the translation process. Finally, we introduce approaches to incorporate pseudo-human feedback in the form of QE predictions in SMT systems. More specifically, we use quality predictions to select the best translation from a number of alternative suggestions produced by SMT systems, and integrate QE predictions into an SMT system decoder in order to guide the translation generation process

Pneumatic ergonomic crutches

Long-term crutch users utilize Lofstrand crutches for locomotion commonly using swing-through or reciprocal gait patterns. Repetitive high forces, hyperextension and ulnar deviation of the wrist, and excessive palmar pressure compressing the median nerve associated with crutch walking have reported to cause discomfort, joint pain, wrist strain, carpal tunnel syndrome and other serious injuries. To address these issues, we developed the pneumatic ergonomic crutches (PEC) that consisted of a pneumatic sleeve orthosis, an energy harvesting system and an energy storage system. The pneumatic sleeve orthosis utilized a soft pneumatic actuator, called fiber-reinforced elastomeric enclosure, coiled around the forearm and secured to the cuff. In the first study, sleeve orthosis performance was examined. Human subject testing indicated significantly improved wrist posture, increased loading sharing to the cuff, reduced and redirected palmar pressure while using the orthosis. In the second study, the fully-developed PEC was presented. The PEC utilized an energy harvesting piston pump to collect pneumatic energy during crutch gait. The collected pneumatic energy was stored into a pneumatic elastomeric accumulator (PEA) inside the crutch shaft, which can be used to inflate the sleeve orthosis to make a self-contained crutch system. We optimized dimensions and specifications of the piston pump and the PEA to minimize the number of gait cycles used to charge the PEA to a target pressure that can be used to fully charge the sleeve orthosis. Bench-top testing was conducted on the PEC and demonstrated the ability of charging the sleeve orthosis using air stored in the PEA after 38 gait cycles. Protocols for future human subject testing to evaluate the system performance of the PEC were also presented

On improving control and efficiency of a portable pneumatically powered ankle-foot orthosis

Ankle foot orthoses (AFOs) are widely used as assistive and/or rehabilitation devices to correct gait of people with lower leg neuromuscular dysfunction and muscle weakness. An AFO is an external device worn on the lower leg and foot that provides mechanical assistance at the ankle joint. Active AFOs are powered devices that provide assistive torque at the ankle joint. We have previously developed the Portable Powered Ankle-Foot Orthosis (PPAFO), which uses pneumatic power via compressed CO2 to provide untethered ankle torque assistance. My dissertation work focused on the development of control strategies for the PPAFO that are robust, applicable to different gait patterns, functional in different gait modes, and energy efficient. Three studies addressing these topics are presented in this dissertation: (1) estimation of the system state during the gait cycle for actuation control; (2) gait mode recognition and control (e.g., stair and ramp descent/ascent); and (3) system analysis and improvement of pneumatic energy efficiency. Study 1 presents the work on estimating the gait state for powered AFO control. The proposed scheme is a state estimator that reliably detects gait events while using only a limited array of sensor data (ankle angle and contact forces at the toe and heel). Our approach uses cross-correlation between a window of past measurements and a learned model to estimate the configuration of the human walker, and detects gait events based on this estimate. The proposed state estimator was experimentally validated on five healthy subjects and with one subject that had neuromuscular impairment. The results highlight that this new approach reduced the root-mean-square error by up to 40% for the impaired subject and up to 49% for the healthy subjects compared to a simplistic direct event controller. Moreover, this approach was robust to perturbations due to changes in walking speed and control actuation. Study 2 proposed a gait mode recognition and control solution to identify a change in walking environment such as stair and ramp ascent/descent. Since portability is a key to the success of the PPAFO as a gait assist device, it is critical to recognize and control for multiple gait modes (i.e., level walking, stair ascent/descent and ramp ascent/descent). While manual mode switching is implemented on most devices, we propose an automatic gait mode recognition scheme by tracking the 3D position of the PPAFO from an inertial measurement unit (IMU). Experimental results indicate that the controller was able to identify the position, orientation and gait mode in real time, and properly control the actuation. The overall recognition success rate was over 97%. Study 3 addressed improving operational runtime by analyzing the system efficiency and proposing an energy harvesting and recycling scheme to save fuel. Through a systematic analysis, the overall system efficiency was determined by deriving both the system operational efficiency and the system component efficiency. An improved pneumatic operation utilized an accumulator to harvest and then recycle the exhaust energy from a previous actuation to power the subsequent actuation. The overall system efficiency was improved from 20.5% to 29.7%, a fuel savings of 31%. Work losses across pneumatic components and solutions to improve them were quantified and discussed. Future work including reducing delay in recognition, exploring faulty recognition, additional options for harvesting human energy, and learning control were proposed