Human activity recognition is the task of identifying a person’s movement from sensors in a wearable device, such as a smartphone, smartwatch, or a medical-grade device. A great method for this task is machine learning, which is the study of algorithms that learn and improve on their own with the help of massive amounts of useful data. These classification models can accurately classify activities with the time-series data from accelerometers and gyroscopes. A significant way to improve the accuracy of these machine learning models is preprocessing the data, essentially augmenting data to make the identification of each activity, or class, easier for the model. <br/>On this topic, this paper explains the design of SigNorm, a new web application which lets users conveniently transform time-series data and view the effects of those transformations in a code-free, browser-based user interface. The second and final section explains my take on a human activity recognition problem, which involves comparing a preprocessed dataset to an un-augmented one, and comparing the differences in accuracy using a one-dimensional convolutional neural network to make classifications.
As life expectancy increases worldwide, age related diseases are becoming greater health concerns. One of the most prevalent age-related diseases in the United States is dementia, with Alzheimer’s disease (AD) being the most common form, accounting for 60-80% of cases. Genetics plays a large role in a person’s risk of developing AD. Familial AD, which makes up less than 1% of all AD cases, is caused by autosomal dominant gene mutations and has almost 100% penetrance. Genetic risk factors are believed to make up about 49%-79% of the risk in sporadic cases. Many different genetic risk factors for both familial and sporadic AD have been identified, but there is still much work to be done in the field of AD, especially in non-Caucasian populations. This review summarizes the three major genes responsible for familial AD, namely APP, PSEN1 and PSEN2. Also discussed are seven identified genetic risk factors for sporadic AD, single nucleotide polymorphisms in the APOE, ABCA7, NEDD9, CASS4, PTK2B, CLU, and PICALM genes. An overview of the main function of the proteins associated with the genes is given, along with the supposed connection to AD pathology.
The purpose of this study is to determine the feasibility of three widely used wearable sensors in research settings for 24 h monitoring of sleep, sedentary, and active behaviors in middle-aged women.
Methods
Participants were 21 inactive, overweight (M Body Mass Index (BMI) = 29.27 ± 7.43) women, 30 to 64 years (M = 45.31 ± 9.67). Women were instructed to wear each sensor on the non-dominant hip (ActiGraph GT3X+), wrist (GENEActiv), or upper arm (BodyMedia SenseWear Mini) for 24 h/day and record daily wake and bed times for one week over the course of three consecutive weeks. Women received feedback about their daily physical activity and sleep behaviors. Feasibility (i.e., acceptability and demand) was measured using surveys, interviews, and wear time.
Results
Women felt the GENEActiv (94.7 %) and SenseWear Mini (90.0 %) were easier to wear and preferred the placement (68.4, 80 % respectively) as compared to the ActiGraph (42.9, 47.6 % respectively). Mean wear time on valid days was similar across sensors (ActiGraph: M = 918.8 ± 115.0 min; GENEActiv: M = 949.3 ± 86.6; SenseWear: M = 928.0 ± 101.8) and well above other studies using wake time only protocols. Informational feedback was the biggest motivator, while appearance, comfort, and inconvenience were the biggest barriers to wearing sensors. Wear time was valid on 93.9 % (ActiGraph), 100 % (GENEActiv), and 95.2 % (SenseWear) of eligible days. 61.9, 95.2, and 71.4 % of participants had seven valid days of data for the ActiGraph, GENEActiv, and SenseWear, respectively.
Conclusion
Twenty-four hour monitoring over seven consecutive days is a feasible approach in middle-aged women. Researchers should consider participant acceptability and demand, in addition to validity and reliability, when choosing a wearable sensor. More research is needed across populations and study designs.
Validity of the Rapid Eating Assessment for Patients for assessing dietary patterns in NCAA athletes
Athletes may be at risk for developing adverse health outcomes due to poor eating behaviors during college. Due to the complex nature of the diet, it is difficult to include or exclude individual food items and specific food groups from the diet. Eating behaviors may better characterize the complex interactions between individual food items and specific food groups. The purpose was to examine the Rapid Eating Assessment for Patients survey (REAP) as a valid tool for analyzing eating behaviors of NCAA Division-I male and female athletes using pattern identification. Also, to investigate the relationships between derived eating behavior patterns and body mass index (BMI) and waist circumference (WC) while stratifying by sex and aesthetic nature of the sport.
Methods
Two independent samples of male (n = 86; n = 139) and female (n = 64; n = 102) collegiate athletes completed the REAP in June-August 2011 (n = 150) and June-August 2012 (n = 241). Principal component analysis (PCA) determined possible factors using wave-1 athletes. Exploratory (EFA) and confirmatory factor analyses (CFA) determined factors accounting for error and confirmed model fit in wave-2 athletes. Wave-2 athletes' BMI and WC were recorded during a physical exam and sport participation determined classification in aesthetic and non-aesthetic sport. Mean differences in eating behavior pattern score were explored. Regression models examined interactions between pattern scores, participation in aesthetic or non-aesthetic sport, and BMI and waist circumference controlling for age and race.
Results
A 5-factor PCA solution accounting for 60.3% of sample variance determined fourteen questions for EFA and CFA. A confirmed solution revealed patterns of Desserts, Healthy food, Meats, High-fat food, and Dairy. Pattern score (mean ± SE) differences were found, as non-aesthetic sport males had a higher (better) Dessert score than aesthetic sport males (2.16 ± 0.07 vs. 1.93 ± 0.11). Female aesthetic athletes had a higher score compared to non-aesthetic female athletes for the Dessert (2.11 ± 0.11 vs. 1.88 ± 0.08), Meat (1.95 ± 0.10 vs. 1.72 ± 0.07), High-fat food (1.70 ± 0.08 vs. 1.46 ± 0.06), and Dairy (1.70 ± 0.11 vs. 1.43 ± 0.07) patterns.
Conclusions
REAP is a construct valid tool to assess dietary patterns in college athletes. In light of varying dietary patterns, college athletes should be evaluated for healthful and unhealthful eating behaviors.
protocols, including within sleep-focused studies. This study seeks to address accuracy of
accelerometer data in detection of the beginnings and ends of sleep bouts in young adults with
polysomnography (PSG) corroboration. An existing algorithm used to differentiate valid/invalid wear
time and detect bouts of sleep has been modified with the goal of maximizing accuracy of sleep bout
detection. Methods: Three key decisions and thresholds of the algorithm have been modified with three
experimental values each being tested. The main experimental variable Sleepwindow controls the
amount of time before and after a determined bout of sleep that is searched for additional sedentary
time to incorporate and consider part of the same sleep bout. Results were compared to PSG and sleep
diary data for absolute agreement of sleep bout start time (START), end time (END) and time in bed
(TIB). Adjustments were made for outliers as well as sleep latency, snooze time, and the sum of both.
Results: Only adjustments made to a sleep window variable yielded altered results. Between a 5-, 15-,
and 30-minute window, a 15-minute window incurred the least error and most agreement to
comparisons for START, while a 5-minute window was best for END and TIB. Discussion: Contrary
to expectation, corrections for snooze, latency, and both did not substantially improve agreement to
PSG. Algorithm-derived estimates of START and END always fell after sleep diary and PSG both,
suggesting either participants’ sedentary behavior beginning and ends were at a delay from sleep and
wake times, or the algorithm estimates consistently later times than appropriate. The inclusion of a
sleep window variable yields substantial variety in results. A 15-minute window appears best at
determining START while a 5-minute window appears best for END and TIB. Further investigation on
the optimal window length per demographic and condition is required.
Current advances in cellular models of neurodegenerative diseases overcome a variety of limitations possessed in animal and post-mortem human models. Human-induced pluripotent stem cells (hiPSCs) provide a platform with cells that can self-renew and differentiate into mature and functional cell types. HiPSCs are at the forefront of neurodegenerative disease research because of their ability to differentiate into neural cell types. Apolipoprotein E (ApoE) is a protein encoded by the APOE gene found on chromosome 19 of the human genome. There are three common polymorphisms in the APOE gene, resulting from a single amino acid change in the protein. The presence of these polymorphisms are studied as associated risk factors of developing AD. Different combinations of these alleles closely relate to the risk a patient has in developing Alzheimer’s disease. The risk associated effects of this gene are primarily investigated, however the protective effects are not examined to the same extent.
This research aims to overcome the existing limitations in cell differentiations and improve cell population purity that limits the variables present in the culture. To do this, this study optimized a differentiation protocol by separating and purifying neuronal cell populations to study the potential protective effects associated with ApoE, a risk factor seen in SAD. This platform aims to use a purified cell population to effectively analyze cell type specific affects of the ApoE risk factor, specifically in neurons.
understanding of the disease pathology, more efficacious drug development and
regenerative medicine as a form of treatment. There are limitations with current
transgenic mouse models of Alzheimer’s disease and the study of post mortem brain tissue of Alzheimer’s diseases patients. Stem cell models can overcome the lack of clinical relevance and impracticality associated with current models. Ideally, the use of stem cell models provides the foundation to study the biochemical and physiological aspects of Alzheimer’s disease, but at the cellular level. Moreover, the future of drug development and disease modeling can be improved by developing a reproducible and well-characterized model of AD that can be scaled up to meet requirements for basic and translational applications. Characterization and analysis of a heterogenic neuronal culture developed from induced pluripotent stem cells calls for the understanding of single cell identity and cell viability. A method to analyze RNA following intracellular sorting was developed in order to analyze single cell identity of a heterogenic population
of human induced pluripotent stem cells and neural progenitor cells. The population was intracellularly stained and sorted for Oct4. RNA was isolated and analyzed with qPCR, which demonstrated expected expression profiles for Oct4+ and Oct4- cells. In addition, a protocol to label cells with pO2 sensing nanoprobes was developed to assess cell viability. Non-destructive nanoprobe up-take by neural progenitor cells was assessed with fluorescent imaging and flow cytometry. Nanoprobe labeled neurons were cultured long-term and continued to fluoresce at day 28. The proof of concept experiments demonstrated will be further expanded upon and utilized in developing a more clinically relevant and cost-effective model of Alzheimer’s disease with downstream applications
in drug development and regenerative medicine.