AIMC Topic: Exercise

Clear Filters Showing 261 to 270 of 375 articles

Measurement of physical activity in clinical practice using accelerometers.

Journal of internal medicine
Accelerometers are commonly used in clinical and epidemiological research for more detailed measures of physical activity and to target the limitations of self-report methods. Sensors are attached at the hip, wrist and thigh, and the acceleration dat...

Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data.

Sensors (Basel, Switzerland)
The physiological monitoring of older people using wearable sensors has shown great potential in improving their quality of life and preventing undesired events related to their health status. Nevertheless, creating robust predictive models from data...

Noninvasive prediction of Blood Lactate through a machine learning-based approach.

Scientific reports
We hypothesized that blood lactate concentration([Lac]) is a function of cardiopulmonary variables, exercise intensity and some anthropometric elements during aerobic exercise. This investigation aimed to establish a mathematical model to estimate [L...

Recognition and Repetition Counting for ComplexPhysical Exercises with Deep Learning.

Sensors (Basel, Switzerland)
Activity recognition using off-the-shelf smartwatches is an important problem in humanactivity recognition. In this paper, we present an end-to-end deep learning approach, able to provideprobability distributions over activities from raw sensor data....

Prediction of Hypoglycemia During Aerobic Exercise in Adults With Type 1 Diabetes.

Journal of diabetes science and technology
BACKGROUND: Fear of exercise related hypoglycemia is a major reason why people with type 1 diabetes (T1D) do not exercise. There is no validated prediction algorithm that can predict hypoglycemia at the start of aerobic exercise.

Exercise-Associated Hyponatremia.

Frontiers of hormone research
Exercise-associated hyponatremia (EAH) refers to below-normal serum sodium concentrations [Na+] that develop during exercise. The pathogenesis of EAH is best described as a spectrum ranging between profound polydipsia to modest sweat sodium losses wi...

Segmenting accelerometer data from daily life with unsupervised machine learning.

PloS one
PURPOSE: Accelerometers are increasingly used to obtain valuable descriptors of physical activity for health research. The cut-points approach to segment accelerometer data is widely used in physical activity research but requires resource expensive ...

Feasibility of Reidentifying Individuals in Large National Physical Activity Data Sets From Which Protected Health Information Has Been Removed With Use of Machine Learning.

JAMA network open
IMPORTANCE: Despite data aggregation and removal of protected health information, there is concern that deidentified physical activity (PA) data collected from wearable devices can be reidentified. Organizations collecting or distributing such data s...

Calibration and validation of accelerometer-based activity monitors: A systematic review of machine-learning approaches.

Gait & posture
BACKGROUND: Objective measures using accelerometer-based activity monitors have been extensively used in physical activity (PA) and sedentary behavior (SB) research. To measure PA and SB precisely, the field is shifting towards machine learning-based...

Effects of a cyborg-type robot suit HAL on cardiopulmonary burden during exercise in normal subjects.

European journal of applied physiology
BACKGROUND: The hybrid assistive limb (HAL) is the world's first cyborg-type robot suit that provides motion assistance to physically challenged patients. HAL is expected to expand the possibilities of exercise therapy for severe cardiac patients who...