Ubiquitous smartphones have created a significant opportunity to form a low-cost wireless Citizen Sensor network and produce big data for monitoring structural integrity and safety under operational and extreme loads. Such data are particularly useful for rapid assessment of structural damage in a large urban setting after a major event such as an earthquake. This study explores the utilization of smartphone accelerometers for measuring structural vibration, from which structural health and post-event damage can be diagnosed. Widely available smartphones are tested under sinusoidal wave excitations with frequencies in the range relevant to civil engineering structures. Large-scale seismic shaking table tests, observing input ground motion and response of a structural model, are carried out to evaluate the accuracy of smartphone accelerometers under operational, white-noise and earthquake excitations of different intensity. Finally, the smartphone accelerometers are tested on a dynamically loaded bridge. The extensive experiments show satisfactory agreements between the reference and smartphone sensor measurements in both time and frequency domains, demonstrating the capability of the smartphone sensors to measure structural responses ranging from low-amplitude ambient vibration to high-amplitude seismic response. Encouraged by the results of this study, the authors are developing a citizen-engaging and data-analytics crowdsourcing platform towards a smartphone-based Citizen Sensor network for structural health monitoring and post-event damage assessment applications.
Although this study was limited because it did not consider all risk factors, categorical chewing number was related independently to body weight increments of more than 10 kg from 20 years of age.
Neurological conditions, such as stroke, can leave the affected individual with hand motor impairment despite intensive treatments. Novel technologies, such as brain-computer interface (BCI), may be able to restore or augment impaired motor behaviors by engaging relevant cortical areas. Here, we developed and tested an electroencephalogram (EEG) based BCI system for control of hand orthosis. An able-bodied subject performed contralateral hand grasping to achieve continuous online control of the hand orthosis, suggesting that the integration of a noninvasive BCI with a hand orthosis is feasible. The adoption of this technology to stroke survivors may provide a novel neurorehabilitation therapy for hand motor impairment in this population.
Stroke survivors are typically affected by hand motor impairment. Despite intensive rehabilitation and spontaneous recovery, improvements typically plateau a year after a stroke. Therefore, novel approaches capable of restoring or augmenting lost motor behaviors are needed. Brain-computer interfaces (BCIs) may offer one such approach by using neurophysiological activity underlying hand movements to control an upper extremity orthosis. To test the performance of such a system, we developed an electroencephalogram-based BCI controlled electrically actuated hand orthosis. Six able-bodied participants voluntarily grasped/relaxed one hand to elicit BCI-mediated closing/opening of the orthosis mounted on the opposite hand. Following a short training/calibration procedure, participants demonstrated real-time, online control of the orthosis by following computer cues. Their performances resulted in an average of 1.15 (standard deviation: 0.85) false alarms and 0.22 (0.36) omissions per minute. Analysis of signals from electrogoniometers mounted on both hands revealed an average correlation between voluntary and BCI-mediated movements of 0.58 (0.13), with all but one online performance being statistically significant. This suggests that a BCI driven hand orthosis is feasible, and therefore should be tested in stroke individuals with hand weakness. If proven viable, this technology may provide a novel approach to the neuro-rehabilitation of hand function after stroke.
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