Deep CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor

Human Activity Recognition (HAR) systems are devised for continuously observing human behavior - primarily in the fields of environmental compatibility, sports injury detection, senior care, rehabilitation, entertainment, and the surveillance in intelligent home settings. Inertial sensors, e.g., accelerometers, linear acceleration, and gyroscopes are frequently employed for this purpose, which are now compacted into smart devices, e.g., smartphones. Since the use of smartphones is so widespread now-a-days, activity data acquisition for the HAR systems is a pressing need. In this article, we have conducted the smartphone sensor-based raw data collection, namely H-Activity , using an Android-OS-based application for accelerometer, gyroscope, and linear acceleration. Furthermore, a hybrid deep learning model is proposed, coupling convolutional neural network and long-short term memory network (CNN-LSTM), empowered by the self-attention algorithm to enhance the predictive capabilities of the system. In addition to our collected dataset ( H-Activity ), the model has been evaluated with some benchmark datasets, e.g., MHEALTH, and UCI-HAR to demonstrate the comparative performance of our model. When compared to other models, the proposed model has an accuracy of 99.93% using our collected H-Activity data, and 98.76% and 93.11% using data from MHEALTH and UCI-HAR databases respectively, indicating its efficacy in recognizing human activity recognition. We hope that our developed model could be applicable in the clinical settings and collected data could be useful for further research.publishedVersio

Childhood disability Parents' perceptions and experiences in Saudi Arabia

SIGLEAvailable from British Library Document Supply Centre-DSC:DX202326 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Deep CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor

Human Activity Recognition (HAR) systems are devised for continuously observing human behavior - primarily in the fields of environmental compatibility, sports injury detection, senior care, rehabilitation, entertainment, and the surveillance in intelligent home settings. Inertial sensors, e.g., accelerometers, linear acceleration, and gyroscopes are frequently employed for this purpose, which are now compacted into smart devices, e.g., smartphones. Since the use of smartphones is so widespread now-a-days, activity data acquisition for the HAR systems is a pressing need. In this article, we have conducted the smartphone sensor-based raw data collection, namely H-Activity , using an Android-OS-based application for accelerometer, gyroscope, and linear acceleration. Furthermore, a hybrid deep learning model is proposed, coupling convolutional neural network and long-short term memory network (CNN-LSTM), empowered by the self-attention algorithm to enhance the predictive capabilities of the system. In addition to our collected dataset ( H-Activity ), the model has been evaluated with some benchmark datasets, e.g., MHEALTH, and UCI-HAR to demonstrate the comparative performance of our model. When compared to other models, the proposed model has an accuracy of 99.93% using our collected H-Activity data, and 98.76% and 93.11% using data from MHEALTH and UCI-HAR databases respectively, indicating its efficacy in recognizing human activity recognition. We hope that our developed model could be applicable in the clinical settings and collected data could be useful for further research

The clinical value of HER-2 overexpression and PIK3CA mutations in the older breast cancer population: a FOCUS study analysis

Studies to confirm the effect of acknowledged prognostic markers in older breast cancer patients are scarce. The aim of this study was to evaluate the prognostic value of HER-2 overexpression and PIK3CA mutations in older breast cancer patients. Female breast cancer patients aged 65 years or older, diagnosed between 1997 and 2004 in a geographical region in The Netherlands, with an invasive, non-metastatic tumour and tumour material available, were included in the study. The primary endpoint was relapse-free period and secondary endpoint was relative survival. Determinants were immunochemical HER-2 scores (0/1+, 2+ or 3+) and PIK3CA as a binary measure. Overall, 1698 patients were included, and 103 had a HER-2 score of 3+. HER-2 overexpression was associated with a higher recurrence risk (5 years recurrence risk 34 % vs. 12 %, adjusted p = 0.005), and a worse relative survival (10 years relative survival 48 % vs. 84 % for HER-2 negative; p = 0.004). PIK3CA mutations had no significant prognostic effect. We showed, in older breast cancer patients, that HER-2 overexpression was significantly associated with a worse outcome, but PIK3CA mutations had no prognostic effect. These results imply that older patients with HER-2 overexpressing breast cancer might benefit from additional targeted anti-HER-2 therapy. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10549-016-3734-y) contains supplementary material, which is available to authorised users