Background and Purpose Outcome measures of mobility for large stroke trials are limited to timed walks for short distances in a laboratory, step counters and ordinal scales of disability and quality of life. Continuous monitoring and outcome measurements of the type and quantity of activity in the community would provide direct data about daily performance, including compliance with exercise and skills practice during routine care and clinical trials. Methods Twelve adults with impaired ambulation from hemiparetic stroke and 6 healthy controls wore triaxial accelerometers on their ankles. Walking speed for repeated outdoor walks was determined by machine-learning algorithms and compared to a stopwatch calculation of speed for distances not known to the algorithm. The reliability of recognizing walking, exercise, and cycling by the algorithms was compared to activity logs. Results A high correlation was found between stopwatch-measured outdoor walking speed and algorithm-calculated speed (Pearson coefficient, 0.98; P=0.001) and for repeated measures of algorithm-derived walking speed (P=0.01). Bouts of walking >5 steps, variations in walking speed, cycling, stair climbing, and leg exercises were correctly identified during a day in the community. Compared to healthy subjects, those with stroke were, as expected, more sedentary and slower, and their gait revealed high paretic-to-unaffected leg swing ratios. Conclusions Test–retest reliability and concurrent and construct validity are high for activity pattern-recognition Bayesian algorithms developed from inertial sensors. This ratio scale data can provide real-world monitoring and outcome measurements of lower extremity activities and walking speed for stroke and rehabilitation studies.

Background Walking-related disability is the most frequent reason for inpatient stroke rehabilitation. Task-related practice is a critical component for improving patient outcomes. Objective To test the feasibility of providing quantitative feedback about daily walking performance and motivating greater skills practice via remote sensing. Methods In this phase III randomized, single blind clinical trial, patients participated in conventional therapies while wearing wireless sensors (tri-axial accelerometers) at both ankles. Activity-recognition algorithms calculated the speed, distance, and duration of walking bouts. Three times a week, therapists provided either feedback about performance on a 10-meter walk (speed-only) or walking speed feedback plus a review of walking activity recorded by the sensors (augmented). Primary outcomes at discharge included total daily walking time, derived from the sensors, and a timed 15-meter walk. Results Sixteen rehabilitation centers in 11 countries enrolled 135 participants over 15 months. Sensors recorded more than 1800 days of therapy, 37,000 individual walking bouts, and 2.5 million steps. No significant differences were found between the two feedback groups in daily walking time (15.1±13.1min vs. 16.6±14.3min, p=0.54) or 15-meter walking speed (0.93±0.47m/s vs. 0.91±0.53m/s, p=0.96). Remarkably, 30% of participants decreased their total daily walking time over their rehabilitation stay. Conclusions In this first trial of remote monitoring of inpatient stroke rehabilitation, augmented feedback beyond speed alone did not increase the time spent practicing or improve walking outcomes. Remarkably modest time was spent walking. Wireless sensing, however, allowed clinicians to audit skills practice and provided ground truth regarding changes in clinically important, mobility-related activities.

Bolts are important fasteners indispensable in the manufacturing field for their advantages, which include convenient assembly and disassembly, easy maintenance, refastenability to prevent looseness, and the avoidance of a phase change in the connected material composition. The precise control of the tightening force in bolts is closely related to the safety and reliability of the connected equipment or structure. Although there are many methods for estimating the tightening force applied to a bolt during assembly, poor accuracy in controlling the preload during the tightening process and a lack of monitoring to determine the residual axial force in service remain issues in evaluating the safety of bolted assemblies. As a nondestructive testing technology, ultrasonic measurement can be applied to successfully address these issues. In order to help researchers understand the theoretical basis and technological development in this field and to equip them to conduct further in-depth research, in this review, the basic knowledge describing the state of stress and deformation of bolts, as well as conventional testing methods are summarized and analyzed. Then, through a review of recent research of the ultrasonic measurement of the axial stress in bolts, the influence of the effective stressed length and temperature are analyzed and proposed methods of calibration and compensation are reviewed. In order to avoid coupling errors caused by traditional piezoelectric transducers, two newly proposed ultrasonic coupling technologies, the electromagnetic acoustic transducer (EMAT) and the permanent mounted transducer system (PMTS), are reviewed. Finally, the new direction of research of the detection of residual axial stress in in-service bolts that have been assembled to yield is discussed.

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Continued rapid progress in cost reduction, energy efficiency, and new data transport architectures for body worn sensors enables remote monitoring of patient activity with critical focus and impact on successful outcomes in healthcare. Monitoring systems, composed of both sensor and signal processing systems, seek to provide the capability to classify subject motion state and characteristics. Monitoring system progress has currently enabled classification of normal gait or abnormal gait within constrained laboratory operating conditions. However, monitoring of subjects in the community (specifically in residential environments remote from the laboratory or urban outdoor environments) has introduced fundamental challenges that have not been solved in the past. These challenges become profoundly more severe when monitoring subjects suffering from impaired gait due to conditions including stroke and other neurological disorders. One of the most important measures required in neurological rehabilitation is the accurate classification of walking speed in the community. Changes in absolute speed directly indicate rehabilitation progress and also directly determine whether an individual may remain safe and functional. Healthcare delivery practice requires that characterization of walking parameters and speed must be provided with reliance only on limited system training data acquisition and time. This paper reports on a primary advance in this capability through development of a novel architecture delivering required high rate, continuous sampling at low cost, with compact sensors and with rapidly deployable systems. Most importantly, this paper introduces a new hierarchical classification system applicable to subjects afflicted with hemiparesis due to stroke and disorders including multiple sclerosis. This system provides accurate classification and characterization of walking mobility invariant to other activities performed at the same time and in the presence of interfering signals induced by gait changes.