Evaluating Pedometer Algorithms on Semi-Regular and Unstructured Gaits.

Pedometers are popular for counting steps as a daily measure of physical activity, however, errors as high as 96% have been reported in previous work. Many reasons for pedometer error have been studied, including walking speed, sensor position on the body and pedometer algorithm, demonstrating some differences in error. However, we hypothesize that the largest source of error may be due to differences in the regularity of gait during different activities. During some activities, gait tends to be regular and the repetitiveness of individual steps makes them easy to identify in an accelerometer signal. During other activities of everyday life, gait is frequently semi-regular or unstructured, which we hypothesize makes it difficult to identify and count individual steps. In this work, we test this hypothesis by evaluating the three most common types of pedometer algorithm on a new data set that varies the regularity of gait. A total of 30 participants were video recorded performing three different activities: walking a path (regular gait), conducting a within-building activity (semi-regular gait), and conducting a within-room activity (unstructured gait). Participants were instrumented with accelerometers on the wrist, hip and ankle. Collectively, 60,805 steps were manually annotated for ground truth using synchronized video. The main contribution of this paper is to evaluate pedometer algorithms when the consistency of gait changes to simulate everyday life activities other than exercise. In our study, we found that semi-regular and unstructured gaits resulted in 5-466% error. This demonstrates the need to evaluate pedometer algorithms on activities that vary the regularity of gait. Our dataset is publicly available with links provided in the introduction and Data Availability Statement.

A comparison of bite size and BMI in a cafeteria setting.

Our study investigated the relationship between BMI and bite size in a cafeteria setting. Two hundred and seventy one participants consumed one meal each. Participants were free to select any food provided by the cafeteria and could return for additional food as desired. Bite weights were measured with a table embedded scale. Data were analyzed with ANOVAs, regressions, Kolmogorov-Smirnov tests, and a repeated measures general linear model for quartile analysis. Obese participants were found to take larger bites than both normal (p=0.002) and overweight participants (p=0.017). Average bite size increased by 0.20g per point increase in BMI. Food bites and drink bites were analyzed individually, showing 0.11g/BMI and 0.23g/BMI slopes, respectively. Quartiles of bites were also analyzed, and a significant interaction was found between normal and obese participants (p=0.034) such that the lower two quartiles were similar, but the upper two quartiles showed an increase in bite size for obese participants. The source of these effects could be the result of a combination of several uncontrolled factors.

Measuring the Consumption of Individual Solid and Liquid Bites Using a Table-Embedded Scale During Unrestricted Eating.

The universal eating monitor (UEM) is a table-embedded scale used to measure grams consumed over time while a person eats. It has been used in laboratory settings to test the effects of anorectic drugs and behavior manipulations such as slowing eating, and to study relationships between demographics and body weight. However, its use requires restricted conditions on the foods consumed and behaviors allowed during eating in order to simplify analysis of the scale data. Individual bites can only be measured when the only interaction with the scale is to carefully remove a single bite of food, consume it fully, and wait a minimum amount of time before the next bite. Other interactions are prohibited such as stirring and manipulating foods, retrieving or placing napkins or utensils on the scale, and in general anything that would change the scale weight that was not related to the consumption of an individual bite. This paper describes a new algorithm that can detect and measure the weight or individual bites consumed during unrestricted eating. The algorithm works by identifying time periods when the scale weight is stable, and then, analyzing the surrounding weight changes. The series of preceding and succeeding weight changes is compared against patterns for single food bites, food mass bites, and drink bites to determine if a scale interaction is due to a bite or some other activity. The method was tested on 271 subjects, each eating a single meal in a cafeteria setting. A total of 24 101 bites were manually annotated in synchronized videos to establish ground truth as to the true, false, and missed detections of bites. Our algorithm correctly detected and weighed approximately 39% of bites with approximately one false positive (FP) per ten actual bites. The improvement compared to the UEM is approximately three times the number of true detections and a 90% reduction in the number of FPs. Finally, an analysis of bites that could not be weighed compared to those that could be weighed revealed no statistically significant difference in average weight. These results suggest that our algorithm could be used to conduct studies using a table scale outside of laboratory or clinical settings and with unrestricted eating behaviors.

Frequency, Not Amplitude, of Latency Affects Subjective Sickness in a Head-Mounted Display.

BACKGROUND: Interactions between frequency and amplitude of latency in head-mounted displays (HMDs) are thought to affect simulator sickness. Many studies have linked system latency to subjective sickness, but recent research has found that at least with the case of inertia-based head tracking technology, latency is not a constant; rather it varies systematically over time due to sensor errors and clock asynchronization. The purpose of this experiment was to further explore the relationship between frequency and amplitude of latency as they relate to subjective sickness experienced in an HMD. METHODS: In a 2 (frequency) × 2 (amplitude) design, 120 subjects were randomly assigned to 4 latency conditions. Frequency of latency was either 0.2 Hz or 1.0 Hz. Amplitude of latency was either 100 ms fixed or 20-100 ms varying. RESULTS: A main effect of frequency of latency was found. Subjects reported greater sickness in the 0.2-Hz frequency conditions (39.0 ± 27.8) compared to the 1-Hz conditions (30.3 ± 17.0). Additionally, 18 subjects withdrew their participation early in the 0.2-Hz conditions compared to 7 in the 1.0-Hz conditions. DISCUSSION: In conclusion, frequency of latency appears to play a role in the experience of sickness in HMDs in both subjective reporting of symptoms and subject performance. The current study confirms results of earlier studies, finding that real motion around a frequency of 0.2 Hz is more sickening than other frequencies. Future work should continue to parse the effects of frequency and amplitude of latency in head-tracked HMDs. Kinsella A, Mattfeld R, Muth E, Hoover A. Frequency, not amplitude, of latency affects subjective sickness in a head-mounted display. Aerosp Med Hum Perform. 2016; 87(7):604-609.