Human activity recognition is influential subject in different fields of human daily life especially in the mobile health. As the smartphone becomes an integrated part of human daily life which has the ability of complex computation, internet connection and also contains a large number of hardware sensors, encourage implementation of the human activity recognition system. Most of the works done in this field imposed the restriction of firmly fixing the smartphone in a certain position on the human body, together with machine learning mechanism, to facilitate the process of classifying human activities from the smartphone sensors raw data. To overcome this restriction, the proposed approach incorporated a smartwatch, fixed on the human ankle, together with smartphone freely carried by the user. The use of smartwatch assisted in providing distinctly separable signal variation from the smartwatch accelerometer and gyroscope sensors raw data which in turn facilitated the use of a threshold-based mechanism to classify 20 various human activities. Furthermore, this work provides a service for remotely real-time monitoring of the user human activities the system is tested with different subjects and achieved an accuracy of 97.5%. .
The aim of our study is to evaluate the diagnostic accuracy of double inversion recovery (DIR) in detection of multiple sclerosis (MS) lesions as well as the correlation between the expanded disability status scale (EDSS) and lesion load measurement detected by DIR, fluid attenuated inversion recovery (FLAIR) and T2 weighted imaging (T2WI) in order to reveal the essential role of DIR sequence in assessing clinical inability as a practicable experiment. A total of 97 patients were assessed on a 3T Siemens Skyra MRI scanner using DIR, FLAIR, and T2W_TSE sequences. EDSS was used to assess the physical disability in patients with MS. The diagnostic accuracy of DIR, FLAIR and T2WI sequences was also determined in different anatomical regions. Sensitivity and specificity were assessed by relative operating characteristics/ receiver operating characteristics (ROC) curve at different cut off points. Spearman correlation was applied to identify the significant relationships between the number of lesions displayed by DIR, FLAIR and T2WI at different regions and EDSS score. Our results pointed out the highest sensitivity (92.9%) and specificity (73.5%) for the number of lesions in infratentorial region at the cut-off point of 4.5 and the highest correlation between the number of lesions and EDSS was observed in infratentorial region (r= 0.584, p<0.001) for DIR sequence. According to the findings of ROC analysis, the number of lesions detected by DIR technique in the infratentorial region is the best predictor of EDSS as a gold standard. DIR can be used as a complementary technique comparing to conventional T2 and FLAIR sequences and describe physical and cognitive dysfunction as well. Due to the higher potential of the DIR sequence to reveal a greater number of MS lesions and to overcome the technical defect of conventional MRI sequences in the diagnosis of cortical lesions, it is recommended that DIR sequences be routinely added to MRI imaging protocols for patients with MS.
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