Whole Body Awareness for Controlling a Robotic Transfemoral Prosthesis

Abstract

Restoring locomotion functionality of transfemoral amputees is essential for early rehabilitation treatment and for preserving mobility and independence in daily life. Research in wearable robotics fostered the development of innovative active mechatronic lower-limb prostheses designed with the goal to reduce the cognitive and physical effort of lower-limb amputees in rehabilitation and daily life activities. To ensure benefits to the users, active mechatronic prostheses are expected to be aware of the user intention and properly interact in a closed human-in-the-loop paradigm. In the state of the art various cognitive interfaces have been proposed to online decode the user's intention. Electromyography in combination with mechanical sensing such as inertial or pressure sensors is a widely adopted solution for driving active mechatronic prostheses. In this framework, researchers also explored targeted muscles re-innervation for an objective-oriented surgical amputation promoting wider usability of active prostheses. However, information kept by the neural component of the cognitive interface deteriorates in a prolonged use scenario due to electrodes-related issues, thereby undermining the correct functionality of the active prosthesis. The objective of this work is to present a novel controller for an active transfemoral prosthesis based on whole body awareness relying on a wireless distributed non-invasive sensory apparatus acting as cognitive interface. A finite-state machine controller based on signals monitored from the wearable interface performs subject-independent intention detection of functional tasks such as ground level walking, stair ascent, and sit-to-stand maneuvres and their main sub-phases. Experimental activities carried out with four transfemoral amputees (among them one dysvascular) demonstrated high reliability of the controller capable of providing 100% accuracy rate in treadmill walking even for weak subjects and low walking speeds. The minimum success rate was of 94.8% in performing sit-to-stand tasks. All the participants showed high confidence in using the transfemoral active prosthesis even without training period thanks to intuitiveness of the whole body awareness controller.

Characterizing Human Box-Lifting Behavior Using Wearable Inertial Motion Sensors

“…For instance, an exoskeleton could be provided with lift information in order to properly support the user during lifting tasks, thereby increasing the effectiveness of such a device [3]. Such intent-detecting controllers have been designed for assistive devices before, such Parri et al's whole-body awareness controller for an active transfemoral prosthesis [13]. Their system uses lower limb kinematics computed using inertial sensors to recognize eight different behaviors, including quiet standing, quiet sitting, step-by-step stair ascent, walking (all the gait phases), sitting down, standing up, initiating walking, and terminating walking.…”

“…Several studies have focused on human gait analysis using wearable inertial measurement units (IMUs-devices consisting of accelerometers, gyroscopes, and optionally magnetometers) for both normal, [8][9][10], and abnormal gaits [11,12]. Gait measurements captured with IMUs can also be used as input to lower limb assistive devices, such as robotic prostheses and exoskeletons [13]. They can be used to detect dangerous conditions, such as falling in geriatric populations [14].…”

Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review

“…Fifty percent of users do still prefer body-powered prostheses, possibly for improved feedback or referred sensation (Biddiss et al, 2007 ). The developments in volitional motor control have outpaced the advancements in sensory feedback for better control of prosthetic arms (Parri et al, 2017 ). Optimization of sensory feedback can provide better results, which include the restoration of a sense of touch that allows amputees to experience the world in a natural way and that also helps in allowing the bionic arm/prosthesis to be incorporated into their body image (Tyler, 2015 ).…”

Load Position and Weight Classification during Carrying Gait Using Wearable Inertial and Electromyographic Sensors

“…The two most common types of human-worn sensors used with assistive devices are inertial measurement units (IMUs), which measure kinematics with a combination of accelerometer and gyroscope (and optionally magnetometer and/or barometer), and electromyographic (EMG) sensors, which noninvasively measure the electrical activity of human muscles. Both IMUs and EMG sensors were previously extensively used to, e.g., control active lower limb prostheses [ 7 ] and classify different reaching motions [ 8 ], and could thus be applied to lifting and carrying as well. The promise of such sensors is supported by previous studies that used optical tracking cameras, which measure similar quantities as IMUs (kinematics); such cameras can, for example, quantify back load [ 9 ], identify risky postures [ 10 ], differentiate between novice and expert load lifters [ 11 ], differentiate between different load weights [ 12 ], and differentiate between holding loads in front or at the sides of the body [ 13 ].…”

“…Once the object has been picked up, does the person carry it in front of their body with both hands, or at the side with one hand? Differentiating between such behaviors would allow assistive devices to better support the user by, e.g., knowing when to begin assisting and how to provide support appropriate for the user’s current activity, similarly to existing control approaches in technologies such as lower limb prostheses and exoskeletons [ 7 , 14 , 15 , 16 , 17 , 18 ].…”