Deep Learning for Classifying Physical Activities from Accelerometer Data

Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening life expectancy. There are minimal medical care and personal trainers’ methods to monitor a patient’s actual physical activity types. To improve activity monitoring, we propose an artificial-intelligence-based approach to classify physical movement activity patterns. In more detail, we employ two deep learning (DL) methods, namely a deep feed-forward neural network (DNN) and a deep recurrent neural network (RNN) for this purpose. We evaluate the two models on two physical movement datasets collected from several volunteers who carried tri-axial accelerometer sensors. The first dataset is from the UCI machine learning repository, which contains 14 different activities-of-daily-life (ADL) and is collected from 16 volunteers who carried a single wrist-worn tri-axial accelerometer. The second dataset includes ten other ADLs and is gathered from eight volunteers who placed the sensors on their hips. Our experiment results show that the RNN model provides accurate performance compared to the state-of-the-art methods in classifying the fundamental movement patterns with an overall accuracy of 84.89% and an overall F1-score of 82.56%. The results indicate that our method provides the medical doctors and trainers a promising way to track and understand a patient’s physical activities precisely for better treatment

Deep Learning for Classifying Physical Activities from Accelerometer Data

Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening life expectancy. There are minimal medical care and personal trainers’ methods to monitor a patient’s actual physical activity types. To improve activity monitoring, we propose an artificial-intelligence-based approach to classify physical movement activity patterns. In more detail, we employ two deep learning (DL) methods, namely a deep feed-forward neural network (DNN) and a deep recurrent neural network (RNN) for this purpose. We evaluate the two models on two physical movement datasets collected from several volunteers who carried tri-axial accelerometer sensors. The first dataset is from the UCI machine learning repository, which contains 14 different activities-of-daily-life (ADL) and is collected from 16 volunteers who carried a single wrist-worn tri-axial accelerometer. The second dataset includes ten other ADLs and is gathered from eight volunteers who placed the sensors on their hips. Our experiment results show that the RNN model provides accurate performance compared to the state-of-the-art methods in classifying the fundamental movement patterns with an overall accuracy of 84.89% and an overall F1-score of 82.56%. The results indicate that our method provides the medical doctors and trainers a promising way to track and understand a patient’s physical activities precisely for better treatment

Effects of a school-based physical activity intervention on academic performance in 14-year old adolescents: a cluster randomized controlled trial – the School in Motion study

Background School-based physical activity interventions evaluating the effect on academic performance usually includes children. We aimed to investigate the effect of a nine-month, school-based physical activity intervention titled School in Motion (ScIM) on academic performance in adolescents. Methods Thirty secondary schools in Norway were cluster-randomized into three groups: the Physically active learning (PAL) group (n = 10), the Don’t worry – Be Happy (DWBH) group (n = 10) or control (n = 10). Target dose in both intervention groups was 120 min/week of additional PA during school hours. Parental consent was obtained from 2084 adolescent students (76%). Standardized national tests in reading and numeracy was conducted at baseline and at the end of the intervention. We used linear mixed model to test intervention effects. We found significant intervention effects in numeracy and reading among students in both interventions when compared with controls. Results The mean difference in change in numeracy was 1.7 (95% CI: 0.9 to 2.5; Cohen’s d = 0.12) and 2.0 (95% CI: 1.4 to 2.7; Cohen’s d = 0.23) points in favour of students in the PAL and DWBH intervention, respectively. Similar results were found for reading, where the mean difference in change was 0.9 (95% CI 0.2 to 1.6; Cohen’s d = 0.06) and 1.1 (95% CI 0.3 to 1.9; Cohen’s d = 0.18) points in favour of students in the PAL and DWBH intervention, respectively. When conducting intention to treat analysis with imputed data the estimates were attenuated and some no longer significant. Conclusion The ScIM study demonstrates that two different school-based PA interventions providing approximately 120 min of additional PA weekly over nine months, significantly improved numeracy and reading performance in 14-year old students compared with controls. However, the results should be interpreted with caution as the effect sizes reported were very small or small and the estimates were attenuated when conducting intention to treat analysis. Despite this, our results are still positive and suggest that PA interventions are viable models to increase academic performance among adolescents

Impact of ethnicity on gestational diabetes identified with the WHO and the modified International Association of Diabetes and Pregnancy Study Groups criteria: a population-based cohort study

Objective The International Association of Diabetes and Pregnancy Study Groups (IADPSG) recently proposed new criteria for diagnosing gestational diabetes mellitus (GDM). We compared prevalence rates, risk factors, and the effect of ethnicity using the World Health Organization (WHO) and modified IADPSG criteria. Methods This was a population-based cohort study of 823 (74% of eligible) healthy pregnant women, of whom 59% were from ethnic minorities. Universal screening was performed at 28±2 weeks of gestation with the 75 g oral glucose tolerance test (OGTT). Venous plasma glucose (PG) was measured on site. GDM was diagnosed as per the definition of WHO criteria as fasting PG (FPG) ≥7.0 or 2-h PG ≥7.8 mmol/l; and as per the modified IADPSG criteria as FPG ≥5.1 or 2-h PG ≥8.5 mmol/l. Results OGTT was performed in 759 women. Crude GDM prevalence was 13.0% with WHO (Western Europeans 11%, ethnic minorities 15%, P=0.14) and 31.5% with modified IADPSG criteria (Western Europeans 24%, ethnic minorities 37%, P< 0.001). Using the WHO criteria, ethnic minority origin was an independent predictor (South Asians, odds ratio (OR) 2.24 (95% confidence interval (CI) 1.26–3.97); Middle Easterners, OR 2.13 (1.12–4.08)) after adjustments for age, parity, and prepregnant body mass index (BMI). This increased OR was unapparent after further adjustments for body height (proxy for early life socioeconomic status), education and family history of diabetes. Using the modified IADPSG criteria, prepregnant BMI (1.09 (1.05–1.13)) and ethnic minority origin (South Asians, 2.54 (1.56–4.13)) were independent predictors, while education, body height and family history had little impact. Conclusion GDM prevalence was overall 2.4-times higher with the modified IADPSG criteria compared with the WHO criteria. The new criteria identified many subjects with a relatively mild increase in FPG, strongly associated with South Asian origin and prepregnant overweigh