Health

This paper applies AI (artificial intelligence) technology to analyze low-dose HRCT (High-resolution chest radiography) data in an attempt to detect COVID-19 pneumonia symptoms. A new model structure is proposed with segmentation of anatomical structures on DNNs-based (deep learning neural network) methods, relying on an abundance of labeled data for proper training.

The dataset comprises up to two weeks of activity data taken from the ankle and foot of 14 people without amputation and 17 people with lower limb amputation.  Walking speed, cadence, and lengths of strides taken at and away from the home were considered in this study.  Data collection came from two wearable sensors, one inertial measurement unit (IMU) placed on the top of the prosthetic or non-dominant foot, and one accelerometer placed on the same ankle.  Location information was derived from GPS and labeled as ‘home’, ‘away’, or ‘unknown’.  The dataset contains raw acce

FallAllD is a large open dataset of human falls and activities of daily living simulated by 15 participants. FallAllD consists of 26420 files collected using three data-loggers worn on the waist, wrist and neck of the subjects. Motion signals are captured using an accelerometer, gyroscope, magnetometer and barometer with efficient configurations that suit the potential applications e.g. fall detection, fall prevention and human activity recognition.

The dataset consists of two populations of fetuses: 160 healthy and 102 Late Intra Uterine Growth Restricted (IUGR). Late IUGR is an adverse pathological condition encompassing chronic hypoxia as a consequence of placental insufficiency, resulting in an abnormal rate of fetal growth. In standard clinical practice, Late IUGR diagnosis can only be suspected in the third trimester and ultimately confirmed at birth. This data collection comprises of a set of 31 Fetal Heart Rate (FHR) indices computed at different time scales and domains accompanied by the clinical diagnosis.