Design of a terrain detection system for foot drop

The ankle foot orthotic (AFO) has been around for centuries. They were created to augment functionality of an ankle damaged due to injury or disease. A common reason a patient might be prescribed an AFO is a condition called foot drop. Foot drop can be caused by many conditions, but the most common reason is a stroke. Foot drop can be characterized by the inability to raise and/or lower a patient\u27s foot. This incapacitation of the patient\u27s foot leads to unnatural gaits and joint fatigue, as well as increasing the patient\u27s likelihood of tripping and becoming seriously injured. Hard plastic AFOs that hold a patient\u27s foot in a neutral position are the current standard for combating foot drop. These AFOs come in many different shapes and sizes, which emphasizes the wide variety in functionality of someone with foot drop. Unfortunately, the restrictive nature of the AFO can cause unnatural movements in the patient\u27s foot; these unnatural tendencies are more exaggerated when walking down stairs and ramps, as the natural gait is to land toe first, the opposite of what the brace allows the patient to do. The purpose of this project is to create a sensor system for an AFO to help identify varying terrain. In the future this information can then be made to control an active AFO. Each terrain type will be first measured by a pair of simple infrared range finder, attached on the lower leg, one range finder looks ahead of the user and the other looks straight down at the ground. Models for the ground conditions can be established by representing each with Fourier series created using RANdom Sample Consensus (RANSAC). RANSAC coefficients will be scaled off the rate of data coming in and gait speed. Each model has a period term so the data can easily be scaled to match the pattern of walking regardless of pace. Gait speed will be measured using the downward facing ankle-mounted rangefinder, but with a threshold to determine when the foot is in contact with the ground. Once this initial set-up is completed, the system can take in data live and provide a prediction of the type of ground the patient is walking over, using pattern recognition techniques. The hope for this project is that if the system can accurately predict the change in ground type from, for example, level walking to walking down a ramp, an AFO could then be made to adjust itself, giving the patient a more natural gait, even when encountering adverse conditions. A byproduct of constantly using a patient\u27s own gait to measure ground type is the ability to track a patient\u27s changing gait over time, giving therapists a valuable new tool for tracking progress in a patient

Design and control of a bio-inspired soft wearable robotic device for ankle-foot rehabilitation

Abstract We describe the design and control of a wearable robotic device powered by pneumatic artificial muscle actuators for use in ankle-foot rehabilitation. The design is inspired by the biological musculoskeletal system of the human foot and lower leg, mimicking the morphology and the functionality of the biological muscle-tendon-ligament structure. A key feature of the device is its soft structure that provides active assistance without restricting natural degrees of freedom at the ankle joint. Four pneumatic artificial muscles assist dorsiflexion and plantarflexion as well as inversion and eversion. The prototype is also equipped with various embedded sensors for gait pattern analysis. For the subject tested, the prototype is capable of generating an ankle range of motion of 27 • (14 • dorsiflexion and 13 • plantarflexion). The controllability of the system is experimentally demonstrated using a linear time-invariant (LTI) controller. The controller is found using an identified LTI model of the system, resulting from the interaction of the soft orthotic device with a human leg, and model-based classical control design techniques. The suitability of the proposed control strategy is demonstrated with several angle-reference following experiments

Design of actuation system and minimization of sensor configuration for gait event detection for Gen 3.0 Portable Powered Ankle-Foot Orthosis (PPAFO)

Powered ankle-foot orthoses (AFOs), which are capable of providing assistive torque at the ankle joint, have significant potential as both assistance and rehabilitation devices. Technology advancements have led to great progression in the development of powered AFOs. Our group had developed the Portable Powered Ankle-Foot Orthosis (PPAFO) that was capable of providing bidirectional assistive torque at the ankle joint. Two generations of the PPAFO were previously developed. Both designs used two different off-the-shelf rotary actuators. This thesis consists of two studies focusing on the development of a new compact higher torque actuation system and the identification of a minimum sensor configuration for gait event detection for a powered AFO. Study 1 presents the design and evaluation of a new actuation system for the PPAFO (Generation 3.0). The actuation system utilized two dual-action linear actuators and a customized gear train. Compared with the previous designs, it generated higher torque and power while providing a thinner lateral profile. The new design had a total weight of (680g) and was capable of generating 32 Nm torque and 110 W power. While running under the same torque and power level as the previous designs, the new design offered better longevity (42.9% and 81.4% increases in normalized run time for test bench emulation and treadmill walking). Although the overall weight of the new actuation system had a 20% increase compared with previous design, it could generate 166.7% more torque and 120% more power, which will enable us to test the system at various torque and power settings. Study 2 investigated the minimum sensor configuration for detecting gait events. Knowledge of the expected orientation and behavior of a limb as related to specific events during the gait cycle (or state timing as a function of the percentage of the gait cycle, % GC) is essential to allow appropriate control of a powered AFO. A total of five sensors were selected (two force sensitive sensors, one ankle angle sensor, and two inertial measurement units (IMU)). The performances of selected sensor configurations were quantified and compared through state-based and event-based approaches in terms of gait state estimation and gait event detection timing, respectively. Gait data were collected from five healthy subjects while walking on a treadmill wearing the Gen 3.0 PPAFO. Results indicated that, while single IMU configurations (located on the shank or foot) both outperformed all other configurations (mean state estimation error: < 2% GC; mean event detection timing error: < 23 ms), the shank IMU was able to detect more gait events than the foot IMU. Since more detectable events could improve the system's robustness (i.e., adjusting to variable speeds) by updating estimation more frequently, a single shank IMU configuration was recommended for powered AFO applications

Ankle-Foot-Orthosis Control in Inclinations and Stairs

A control procedure is proposed for an ankle-footorthosis (AFO) for different gait situations, such as inclinations and stairs. This paper presents a novel AFO control of the ankle angle. A magneto-rheological damper was used to achieve ankle damping during foot down and locking at swing, thereby avoiding foot slap as well as foot drop. The controller used feedback from the ankle angle only. Still it was capable of not only adjusting damping within a gait step but also changing control behavior depending on level walking, ascending and descending stairs. As a consequence, toe strike was possible in stair gait as opposed to heel strike in level walking. Tests verified the expected behavior in stair gait and in level walking where gait speed and ground inclinations varied. The self-adjusted AFO is believed to improve gait comfort in slopes and stairs.©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.</p

Studies on gait control using a portable pneumatically powered ankle-foot orthosis (PPAFO) during human walking

A powered ankle-foot orthosis (AFO) can be very useful for people with neuromuscular injury. Control of powered AFOs will be more efficient to provide assistance to individuals with lower limb muscle impairments if we can identify different gait events during walking. A walking or gait cycle can be divided into multiple phases and sub-phases by proper gait event detection, and these phases/sub-phases are associated with one of the three main functional tasks during the gait cycle: loading response, forward propulsion, and limb advancement. The gait cycle of one limb can also be characterized by examining the limb’s behavior over one stride, which can be quantified as 0% to 100% of a gait cycle (GC). One easy approach to identify gait events is by checking whether sensor signals go above/below a predetermined threshold. By estimation of a walker’s instantaneous state, as represented by a specific percentage of the gait cycle (from states 0 to 100, which correlate with 0% to 100% GC), we can efficiently detect the various gait events more accurately. Our Human Dynamics and Controls Laboratory previously developed the portable pneumatically powered ankle-foot orthosis (PPAFO), which was capable of providing torque in both plantarflexion and dorsiflexion directions at the ankle. There were three types of sensor attached with the PPAFO (two force sensitive resistors and an angle sensor). In this dissertation, three aspects of effective control strategies for the PPAFO have been proposed. In the first study, two improved and reliable state estimators (Modified Fractional Time (MFT) and Artificial Neural Network (ANN)) were proposed for identifying when the limb with the PPAFO was at a certain percentage of the gait cycle. A correct estimation of percentage of gait cycle will assist with detecting specific gait events more accurately. The performance of new estimators was compared to a previously developed Fractional Time state estimation technique. To control a powered AFO using these estimators, however, detection of proper actuation timing is necessary. In the second study, a supervised learning algorithm to classify the appropriate start timing for plantarflexor actuation was proposed. Proper actuation timing has only been addressed in the literature in terms of functional efficiency or metabolic cost during walking. In this study, we will explore identifying the plantarflexor actuation timing in terms of biomechanics outcomes of human walking using a machine learning based algorithm. The third study investigated the recognition of different gait modes encountered during walking. The actuation scheme plays a significant role in walking on level ground, stair descent or stair ascent modes. The wrong actuation scheme for a given mode can cause falls or trips. A gait mode recognition technique was developed for detecting these different modes by attaching an inertial measurement unit and using a classifier based on artificial neural networks. This new algorithm improves upon the current one step delay limitation found as a drawback of a previously developed technique. Overall, this dissertation focused on addressing some important issues related to control of powered AFO that ultimately will help to assist people wearing the device in daily life situations during walking. The proposed approaches and algorithms introduced in this dissertation showed very promising results that proved that these methods can successfully improve the control system of powered AFOs

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