A Survey of Teleceptive Sensing for Wearable Assistive Robotic Devices

Abstract

Teleception is defined as sensing that occurs remotely, with no physical contact with the object being sensed. To emulate innate control systems of the human body, a control system for a semi- or fully autonomous assistive device not only requires feedforward models of desired movement, but also the environmental or contextual awareness that could be provided by teleception. Several recent publications present teleception modalities integrated into control systems and provide preliminary results, for example, for performing hand grasp prediction or endpoint control of an arm assistive device; and gait segmentation, forward prediction of desired locomotion mode, and activity-specific control of a prosthetic leg or exoskeleton. Collectively, several different approaches to incorporating teleception have been used, including sensor fusion, geometric segmentation, and machine learning. In this paper, we summarize the recent and ongoing published work in this promising new area of research.

Subject- and Environment-Based Sensor Variability for Wearable Lower-Limb Assistive Devices

“…In this case the sensor selected for use was a small ToF-based sensor, which was chosen to be complementary to the approaches used in our previous works [13,14] and to provide contextual data that could ultimately be implemented for not only accurate forward prediction, but also gait trajectory predictions, early planning of desired activities, and even potentially for simultaneous localization and mapping (SLAM) of select environments (though in this work the use of this sensor for forward prediction is primarily considered). However, there are a number of potential environmental sensors that could have been selected for use in this work, and a more extensive analysis of the best sensors and configurations for wearable assistive robotics is presented in our upcoming Review paper [35].…”

Sensor Fusion in Assistive and Rehabilitation Robotics

“…Finally, the eighth paper, by Krausz and Hargrove [ 8 ], carries out a survey of teleceptive (remote, contactless) sensing for wearable assistive robotic devices. Related to the aforementioned sixth paper, the authors argue that teleceptive sensing has high potential for providing environmental and contextual awareness that could greatly improve the effectiveness and robustness of assistive robots.…”

ExoNet Database: Wearable Camera Images of Human Locomotion Environments

“…Hundreds of millions of individuals worldwide have mobility impairments resulting from degenerative aging and neuro-musculoskeletal disorders like spinal cord injury, osteoarthritis, Parkinson’s disease, and cerebral palsy (Grimmer et al, 2019). Fortunately, newly-developed powered lower-limb exoskeletons and prostheses can allow otherwise wheelchair-bound seniors and rehabilitation patients to perform movements that involve net positive mechanical work (e.g., climbing stairs and standing from a seated position) using onboard actuators and intelligent control systems (Krausz and Hargrove, 2019; Laschowski and Andrysek, 2018; Tucker et al, 2015; Young and Ferris, 2017; Zhang et al, 2019a). Generally speaking, the high-level controller recognizes the patient’s locomotion mode (intention) by analyzing real-time measurements from wearable sensors using machine learning algorithms.…”

“…This control level typically comprises a finite state machine, which implements a discrete parametrized control law (e.g., joint position or mechanical impedance control) for each different locomotion mode. Finally, the low-level controller tracks the reference trajectories and minimizes the signal error by modulating the device actuators using feedforward and feedback control loops (Krausz and Hargrove, 2019; Laschowski and Andrysek, 2018; Tucker et al, 2015; Young and Ferris, 2017; Zhang et al, 2019a).…”

“…These human-controlled methods can be time-consuming, inconvenient, and cognitively demanding. Researchers have recently developed automated locomotion mode recognition systems using wearable sensors like inertial measurement units (IMUs) and surface electromyography (EMG) to automatically switch between different locomotion modes (Krausz and Hargrove, 2019; Laschowski and Andrysek, 2018; Tucker et al, 2015; Young and Ferris, 2017; Zhang et al, 2019a). Whereas mechanical and inertial sensors respond to the patient’s movements, the electrical potentials of biological muscles, as recorded using surface EMG, precede movement initiation and thus could (marginally) predict locomotion mode transitions.…”