Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial Sensors.

We present a benchmark dataset for evaluating physical human activity recognition methods from wrist-worn sensors, for the specific setting of basketball training, drills, and games. Basketball activities lend themselves well for measurement by wrist-worn inertial sensors, and systems that are able to detect such sport-relevant activities could be used in applications of game analysis, guided training, and personal physical activity tracking. The dataset was recorded from two teams in separate countries (USA and Germany) with a total of 24 players who wore an inertial sensor on their wrist, during both a repetitive basketball training session and a game. Particular features of this dataset include an inherent variance through cultural differences in game rules and styles as the data was recorded in two countries, as well as different sport skill levels since the participants were heterogeneous in terms of prior basketball experience. We illustrate the dataset's features in several time-series analyses and report on a baseline classification performance study with two state-of-the-art deep learning architectures.

Global Impact of COVID-19 Pandemic on Physical Activity Habits of Competitive Runners: An Analysis of Wearable Device Data.

The COVID-19 pandemic resulted in government restrictions that altered the lifestyle of people worldwide. Studying the impact of these restrictions on exercise behaviors will improve our understanding of the environmental factors that influence individuals' physical activity (PA). We conducted a retrospective analysis using an stringency index of government pandemic policies developed by Oxford University and digitally-logged PA data from more than 7000 runners collected using a wearable exercise-tracking device to compare the relationship between strictness of lockdowns and exercise habits on a global scale. Additionally, the time-of-day of PA globally, and activity-levels of PA in 14 countries, are compared between the pre-pandemic year of 2019 and the first pandemic year of 2020. We found that during the pandemic year there was a major shift in the time-of-day that runners exercised, with significantly more activity counts logged during standard working hours on workdays (p < 0.001) and fewer activities during the same time frame on weekends (p < 0.001). Of the countries examined, Italy and Spain had among the most strict lockdowns and suffered the largest decreases in activity counts, whereas France experienced a minimal decrease in activity counts despite enacting a strict lockdown with certain allowances. This study suggests that there are several factors affecting PA of dedicated runners, including government policy, workplace policy, and cultural norms.

L-Ferritin: One Gene, Five Diseases; from Hereditary Hyperferritinemia to Hypoferritinemia-Report of New Cases.

Ferritin is a multimeric protein composed of light (L-ferritin) and heavy (H-ferritin) subunits that binds and stores iron inside the cell. A variety of mutations have been reported in the L-ferritin subunit gene (FTL gene) that cause the following five diseases: (1) hereditary hyperferritinemia with cataract syndrome (HHCS), (2) neuroferritinopathy, a subtype of neurodegeneration with brain iron accumulation (NBIA), (3) benign hyperferritinemia, (4) L-ferritin deficiency with autosomal dominant inheritance, and (5) L-ferritin deficiency with autosomal recessive inheritance. Defects in the FTL gene lead to abnormally high levels of serum ferritin (hyperferritinemia) in HHCS and benign hyperferritinemia, while low levels (hypoferritinemia) are present in neuroferritinopathy and in autosomal dominant and recessive L-ferritin deficiency. Iron disturbances as well as neuromuscular and cognitive deficits are present in some, but not all, of these diseases. Here, we identified two novel FTL variants that cause dominant L-ferritin deficiency and HHCS (c.375+2T > A and 36_42delCAACAGT, respectively), and one previously reported variant (Met1Val) that causes dominant L-ferritin deficiency. Globally, genetic changes in the FTL gene are responsible for multiple phenotypes and an accurate diagnosis is useful for appropriate treatment. To help in this goal, we included a diagnostic algorithm for the detection of diseases caused by defects in FTL gene.