IEEE Neural Systems and Rehabilitation Engineering
TOC Alert for Publication# 7333
Updated: 31 weeks 2 hours ago
A Mobile Kalman-Filter Based Solution for the Real-Time Estimation of Spatio-Temporal <?Pub _newline ?>Gait Parameters
Gait impairments are among the most disabling symptoms in several musculoskeletal and neurological conditions, severely limiting personal autonomy. Wearable gait sensors have been attracting attention as diagnostic tool for gait and are emerging as promising tool for tutoring and guiding gait execution. If their popularity is continuously growing, still there is room for improvement, especially towards more accurate solutions for spatio-temporal gait parameters estimation. We present an implementation of a zero-velocity-update gait analysis system based on a Kalman filter and off-the-shelf shoe-worn inertial sensors. The algorithms for gait events and step length estimation were specifically designed to comply with pathological gait patterns. More so, an Android app was deployed to support fully wearable and stand-alone real-time gait analysis. Twelve healthy subjects were enrolled to preliminarily tune the algorithms; afterwards sixteen persons with Parkinson's disease were enrolled for a validation study. Over the 1314 strides collected on patients at three different speeds, the total root mean square difference on step length estimation between this system and a gold standard was 2.9%. This shows that the proposed method allows for an accurate gait analysis and paves the way to a new generation of mobile devices usable anywhere for monitoring and intervention.
This paper investigates a fall detection system based on the integration of an inertial measurement unit with a barometric altimeter (BIMU). The vertical motion of the body part the BIMU was attached to was monitored on-line using a method that delivered drift-free estimates of the vertical velocity and estimates of the height change from the floor. The experimental study included activities of daily living of seven types and falls of five types, simulated by a cohort of 25 young healthy adults. The downward vertical velocity was thresholded at 1.38 m/s, yielding 80% sensitivity (SE), 100% specificity (SP) and a mean prior-to-impact time of 157 ms (range 40–300 ms). The soft falls, i.e., those with downward vertical velocity above 0.55 m/s and below 1.38 m/s were analyzed post-impact. Six fall detection methods, tuned to achieve 100% SE, were considered to include features of impact, change of posture and height, singularly or in association with one another. No single feature allowed for 100% SP. The detection accuracy marginally improved when the height change was considered in association with either the impact or the change of posture; the post-impact fall detection method that analyzed the impact and the change of posture together achieved 100% SP.
Measurement of Contact Behavior Including Slippage of Cuff When Using Wearable Physical Assistant Robot
Continuous use of wearable robots can cause skin injuries beneath the cuffs of robots. To prevent such injuries, understanding the contact behavior of the cuff is important. Thus far, this contact behavior has not been studied because of the difficulty involved in measuring the slippage under the cuff. In this study, for the first time, the relative displacement, slippage, and interaction force and moment at the thigh cuff of a robot during sit-to-stand motion were measured using an instrumented cuff, which was developed for this purpose. The results indicated that the slippage and relative displacement under the cuff was uneven because of the rotation of the cuff, which suggests that the risk of skin injuries is different at different positions. Especially, the skin closer to the hip showed larger dynamism, with a maximum slippage of approximately 10 mm and a displacement of 20 mm during motion. Another important phenomenon was the individual difference among subjects. During motion, the interaction force, moment, and slippage of some subjects suddenly increased. Such behavior results in stress concentration, which increases the risk of skin injuries. These analyses are intended to understand how skin injuries are caused and to design measures to prevent such injuries.
The goal of this study was to characterize the effects of stimulation parameters and multielectrode stimulation on selectivity, range of motion, recruitment characteristics, and fatigue during intraspinal microstimulation (ISMS). A custom-made multielectrode array was implanted into the activation pool of the rat dorsiflexor muscle where the stimulation produced the highest movement range on the ankle joint and the least effect on the other joints. The results show that the selectivity could be significantly enhanced using multielectrode stimulation strategy. Moreover, the fatigue was significantly reduced using multielectrode synchronous stimulation with respect to single-electrode stimulation. For a given charge, stimulation with higher current amplitude and shorter pulse duration produced greater range of motion than that with lower amplitude and longer pulse duration. However, the stimulation with shorter duration caused greater fatigue than that with longer. In addition, there was a significant difference in time constant of spinal response obtained with different pulse amplitudes during pulse width (PW) modulation. The time constant decreased with increasing pulse amplitude. However, there was no significant effect of pulse duration on time constant during pulse amplitude (PA) modulation. The results suggest that the motor neurons (MNs) within the spinal cord can be recruited according to size principle by appropriate selection of stimulation parameters. Based on these results an efficient stimulation strategy can be designed for control of movement performance (i.e., speed of movement, fatigue, range of motion, and selectivity) during ISMS.
Potable electroencephalography (EEG) devices have become critical for important research. They have various applications, such as in brain–computer interfaces (BCI). Numerous recent investigations have focused on the development of dry sensors, but few concern the simultaneous attachment of high-density dry sensors to different regions of the scalp to receive qualified EEG signals from hairy sites. An inflatable and wearable wireless 32-channel EEG device was designed, prototyped, and experimentally validated for making EEG signal measurements; it incorporates spring-loaded dry sensors and a novel gasbag design to solve the problem of interference by hair. The cap is ventilated and incorporates a circuit board and battery with a high-tolerance wireless (Bluetooth) protocol and low power consumption characteristics. The proposed system provides a 500/250 Hz sampling rate, and 24 bit EEG data to meet the BCI system data requirement. Experimental results prove that the proposed EEG system is effective in measuring audio event-related potential, measuring visual event-related potential, and rapid serial visual presentation. Results of this work demonstrate that the proposed EEG cap system performs well in making EEG measurements and is feasible for practical applications.