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Feasibility of At-Home Hand Arm Bimanual Intensive Training in Virtual Reality: Case Study

Feasibility of At-Home Hand Arm Bimanual Intensive Training in Virtual Reality: Case Study

Finally, remote data monitoring of hand and arm function through 3 D accelerometers in the VR hardware was added to explore the use of actigraphy as a measure of movement characteristics. Actigraphy uses accelerometer-based devices to monitor and quantify physical activity levels and movement patterns over time. By wearing an actigraph device, individuals can track their daily activity levels, including intensity of movement, duration of physical activity, and periods of rest or sedentary behavior.

James E Gehringer, Anne Woodruff Jameson, Hailey Boyer, Jennifer Konieczny, Ryan Thomas, James Pierce III, Andrea B Cunha, Sandra Willett

JMIR Form Res 2024;8:e57588

Comprehensive Assessment and Early Prediction of Gross Motor Performance in Toddlers With Graph Convolutional Networks–Based Deep Learning: Development and Validation Study

Comprehensive Assessment and Early Prediction of Gross Motor Performance in Toddlers With Graph Convolutional Networks–Based Deep Learning: Development and Validation Study

In this study, we evaluated each gross motor behavior and assessed each child’s overall gross motor performance status using movement videos of toddlers aged 18-35 months. To the best of our knowledge, this study is the first to predict the overall gross motor behavior status using pediatric gross motor movement videos at ages younger than 3 years.

Sulim Chun, Sooyoung Jang, Jin Yong Kim, Chanyoung Ko, JooHyun Lee, JaeSeong Hong, Yu Rang Park

JMIR Form Res 2024;8:e51996

Characteristic Changes of the Stance-Phase Plantar Pressure Curve When Walking Uphill and Downhill: Cross-Sectional Study

Characteristic Changes of the Stance-Phase Plantar Pressure Curve When Walking Uphill and Downhill: Cross-Sectional Study

Wearables such as pressure insoles are increasingly used to study gait and movement, as well as for fall detection, fall classification, and fall risk assessment in the daily life of patients, and furthermore for lifestyle and health monitoring [1,3,23-27]. Long-term monitoring, especially if combined with additional sensors, may produce large amounts of data that require advanced strategies for analyses.

Christian Wolff, Patrick Steinheimer, Elke Warmerdam, Tim Dahmen, Philipp Slusallek, Christian Schlinkmann, Fei Chen, Marcel Orth, Tim Pohlemann, Bergita Ganse

J Med Internet Res 2024;26:e44948

Differences in Brain Activity and Body Movements Between Virtual Reality and Offline Exercise: Randomized Crossover Trial

Differences in Brain Activity and Body Movements Between Virtual Reality and Offline Exercise: Randomized Crossover Trial

Additionally, four types of movement lengths were measured: (1) right arm movement length, (2) left arm movement length, (3) right leg movement length, and (4) left leg movement length (Figure 3). The total angle and movement length for simple behaviors, 2 complex behaviors, and 3 complex behaviors were calculated using each angle and movement length involved in each behavior. For example, for simple behaviors, 8 angles and 4 lengths were used to calculate the total angle and movement lengths.

Hee Jin Kim, Jea Woog Lee, Gangta Choi, Junghoon Huh, Doug Hyun Han

JMIR Serious Games 2023;11:e40421

Defining Activity Thresholds Triggering a “Stand Hour” for Apple Watch Users: Cross-Sectional Study

Defining Activity Thresholds Triggering a “Stand Hour” for Apple Watch Users: Cross-Sectional Study

On Apple smartwatches, an SH measurement is intended to be recorded by performing at least 1 minute of movement within an hour. However, there is no further guidance provided by Apple regarding the quantity or type of movement needed to achieve this. It is thought that Apple watches use an accelerometer and gyroscope to identify standing and movement [19].

Katy Lyons, Alison Hau Hei Man, David Booth, Graham Rena

JMIR Form Res 2024;8:e53806

Markerless Motion Capture to Quantify Functional Performance in Neurodegeneration: Systematic Review

Markerless Motion Capture to Quantify Functional Performance in Neurodegeneration: Systematic Review

Importantly, their ability to capture movement unobtrusively is a key benefit for user compliance [8], particularly when working with individuals with cognitive impairments. MMC is attractive for health care and research use, such as monitoring functional performance loss or improvement in neurodegenerative diseases.

Julian Jeyasingh-Jacob, Mark Crook-Rumsey, Harshvi Shah, Theresita Joseph, Subati Abulikemu, Sarah Daniels, David J Sharp, Shlomi Haar

JMIR Aging 2024;7:e52582

HD-DRUM, a Tablet-Based Drumming Training App Intervention for People With Huntington Disease: App Development Study

HD-DRUM, a Tablet-Based Drumming Training App Intervention for People With Huntington Disease: App Development Study

Striatal atrophy [1] and white matter degeneration [2] are observed many years before the onset of movement symptoms. These early brain changes are accompanied by impairments in psychomotor speed and executive functions [3,4] including problems in decision-making, multitasking, and motor sequence learning, all of which may hamper a person’s everyday functional abilities such as working capacity [5].

Claudia Metzler-Baddeley, Monica Busse, Cheney Drew, Philip Pallmann, Jaime Cantera, Vasileios Ioakeimidis, Anne Rosser

JMIR Form Res 2023;7:e48395

Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things–Based Thermostat Data and Google Mobility Insights

Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things–Based Thermostat Data and Google Mobility Insights

These large data sets, equivalent to “big data” in volumes, are now being analyzed to describe human movement patterns, characteristics (such as sleep, stress, and activity), and interactions. Google Maps is the most popular navigation app in the United States and Canada. The app surpassed 23 million downloads in 2020 with 154.4 million monthly users. Google passively generates and collects over 20 million pieces of mobility data per day.

Kirti Sundar Sahu, Joel A Dubin, Shannon E Majowicz, Sam Liu, Plinio P Morita

JMIR Public Health Surveill 2024;10:e46903

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