The VISTA datasets, a combination of inertial sensors and depth cameras data for activity recognition

Fiorini, Laura; Loizzo, Federica G. C.; Sorrentino, Alessandra; Rovini, Erika; Nuovo, Alessandro Di; Cavallo, Filippo

doi:10.1038/s41597-022-01324-3

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…The dataset includes RGB videos, depth maps, and the skeleton sequences of 25 body joints. Fiorini et al proposed a gesture and activity recognition dataset named VISTA, which is a combination of inertial sensor and depth camera data in [ 131 ]. The dataset includes 7682 action instances for the training phase and 3361 action instances for the testing phase.…”

Section: Survey Of Benchmark Datasetsmentioning

confidence: 99%

In-Home Older Adults’ Activity Pattern Monitoring Using Depth Sensors: A Review

Momin

Sufian

Barman

et al. 2022

Sensors

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The global population is aging due to many factors, including longer life expectancy through better healthcare, changing diet, physical activity, etc. We are also witnessing various frequent epidemics as well as pandemics. The existing healthcare system has failed to deliver the care and support needed to our older adults (seniors) during these frequent outbreaks. Sophisticated sensor-based in-home care systems may offer an effective solution to this global crisis. The monitoring system is the key component of any in-home care system. The evidence indicates that they are more useful when implemented in a non-intrusive manner through different visual and audio sensors. Artificial Intelligence (AI) and Computer Vision (CV) techniques may be ideal for this purpose. Since the RGB imagery-based CV technique may compromise privacy, people often hesitate to utilize in-home care systems which use this technology. Depth, thermal, and audio-based CV techniques could be meaningful substitutes here. Due to the need to monitor larger areas, this review article presents a systematic discussion on the state-of-the-art using depth sensors as primary data-capturing techniques. We mainly focused on fall detection and other health-related physical patterns. As gait parameters may help to detect these activities, we also considered depth sensor-based gait parameters separately. The article provides discussions on the topic in relation to the terminology, reviews, a survey of popular datasets, and future scopes.

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Section: Survey Of Benchmark Datasetsmentioning

confidence: 99%

In-Home Older Adults’ Activity Pattern Monitoring Using Depth Sensors: A Review

Momin

Sufian

Barman

et al. 2022

Sensors

View full text Add to dashboard Cite

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Trust in Robot Benchmarking and Benchmarking for Trustworthy Robots

Thoduka,

Nair,

Caleb-Solly

et al. 2024

Studies in Computational Intelligence

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A multimodal dataset of real world mobility activities in Parkinson’s disease

Morgan,

Tonkin,

Masullo

et al. 2023

Sci Data

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Parkinson’s disease (PD) is a neurodegenerative disorder characterised by motor symptoms such as gait dysfunction and postural instability. Technological tools to continuously monitor outcomes could capture the hour-by-hour symptom fluctuations of PD. Development of such tools is hampered by the lack of labelled datasets from home settings. To this end, we propose REMAP (REal-world Mobility Activities in Parkinson’s disease), a human rater-labelled dataset collected in a home-like setting. It includes people with and without PD doing sit-to-stand transitions and turns in gait. These discrete activities are captured from periods of free-living (unobserved, unstructured) and during clinical assessments. The PD participants withheld their dopaminergic medications for a time (causing increased symptoms), so their activities are labelled as being “on” or “off” medications. Accelerometry from wrist-worn wearables and skeleton pose video data is included. We present an open dataset, where the data is coarsened to reduce re-identifiability, and a controlled dataset available on application which contains more refined data. A use-case for the data to estimate sit-to-stand speed and duration is illustrated.

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The VISTA datasets, a combination of inertial sensors and depth cameras data for activity recognition

Cited by 3 publications

References 16 publications

In-Home Older Adults’ Activity Pattern Monitoring Using Depth Sensors: A Review

In-Home Older Adults’ Activity Pattern Monitoring Using Depth Sensors: A Review

Trust in Robot Benchmarking and Benchmarking for Trustworthy Robots

A multimodal dataset of real world mobility activities in Parkinson’s disease

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