Background: Physical behavior researchers using motion sensors often use acceleration summaries to visualize, clean, and interpret data. Such output is dependent on device specifications (e.g., dynamic range, sampling rate) and/or are proprietary, which invalidate cross-study comparison of findings when using different devices. This limits flexibility in selecting devices to measure physical activity, sedentary behavior, and sleep. Purpose: Develop an open-source, universal acceleration summary metric that accounts for discrepancies in raw data among research and consumer devices. Methods: We used signal processing techniques to generate a Monitor-Independent Movement Summary unit (MIMS-unit) optimized to capture normal human motion. Methodological steps included raw signal harmonization to eliminate inter-device variability (e.g., dynamic g-range, sampling rate), bandpass filtering (0.2–5.0 Hz) to eliminate non-human movement, and signal aggregation to reduce data to simplify visualization and summarization. We examined the consistency of MIMS-units using orbital shaker testing on eight accelerometers with varying dynamic range (±2 to ±8 g) and sampling rates (20–100 Hz), and human data (N = 60) from an ActiGraph GT9X. Results: During shaker testing, MIMS-units yielded lower between-device coefficient of variations than proprietary ActiGraph and ENMO acceleration summaries. Unlike the widely used ActiGraph activity counts, MIMS-units were sensitive in detecting subtle wrist movements during sedentary behaviors. Conclusions: Open-source MIMS-units may provide a means to summarize high-resolution raw data in a device-independent manner, thereby increasing standardization of data cleaning and analytical procedures to estimate selected attributes of physical behavior across studies.

This paper extends previous work automatically detecting stereotypical motor movements (SMM) in individuals on the autism spectrum. Using three-axis accelerometer data obtained through wearable wireless sensors, we compare recognition results for two different classifiers -Support Vector Machine and Decision Tree -in combination with different feature sets based on time-frequency characteristics of accelerometer data. We use data collected from six individuals on the autism spectrum who participated in two different studies conducted three years apart in classroom settings, and observe an average accuracy across all participants over time ranging from 81.2% (TPR: 0.91; FPR: 0.21) to 99.1% (TPR: 0.99; FPR: 0.01) for all combinations of classifiers and feature sets. We also provide analyses of kinematic parameters associated with observed movements in an attempt to explain classifier-feature specific performance. Based on our results, we conclude that real-time, person-dependent, adaptive algorithms are needed in order to accurately and consistently measure SMM automatically in individuals on the autism spectrum over time in real-word settings.

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PurposePeripheral prisms (p-prisms) shift peripheral portions of the visual field of one eye, providing visual field expansion for patients with hemianopia. However, patients rarely show adaption to the shift, incorrectly localizing objects viewed within the p-prisms. A pilot evaluation of a novel computerized perceptual-motor training program aiming to promote p-prism adaption was conducted.MethodsThirteen patients with hemianopia fitted with 57Δ oblique p-prisms completed the training protocol. They attended six 1-hour visits reaching and touching peripheral checkerboard stimuli presented over videos of driving scenes while fixating a central target. Performance was measured at each visit and after 3 months.ResultsThere was a significant reduction in touch error (P = 0.01) for p-prism zone stimuli from pretraining median of 16.6° (IQR 12.1°–19.6°) to 2.7° ( IQR 1.0°–8.5°) at the end of training. P-prism zone reaction times did not change significantly with training (P > 0.05). P-prism zone detection improved significantly (P = 0.01) from a pretraining median 70% (IQR 50%–88%) to 95% at the end of training (IQR 73%–98%). Three months after training improvements had regressed but performance was still better than pretraining.ConclusionsImproved pointing accuracy for stimuli detected in prism-expanded vision of patients with hemianopia wearing 57Δ oblique p-prisms is possible and training appears to further improve detection.Translational RelevanceThis is the first use of this novel software to train adaptation of visual direction in patients with hemianopia wearing peripheral prisms.