Daily motionless activities: A dataset with accelerometer, magnetometer, gyroscope, environment, and GPS data

Pires, Ivan Miguel; García, Nuno M.; Zdravevski, Eftim; Lameski, Petre

doi:10.1038/s41597-022-01213-9

Cited by 7 publications

(7 citation statements)

References 19 publications

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“… users ContextAct@A4H (2017) 41 Door, Light, Temperature, Take Shower, Toilet use, Sleep, Cook, Single user Co2 levels, Appliances states Leave Home, Wash Dishes, Eat, Work E-care@home (2017) 42 Motion, Light, Pressure, Sitting, Moving, Watching TV, Burning, Single user Luminosity, Heartbeat simulator Exercising, Cooking, Eating, etc. Motionless Accelerometer, Magnetometer, Sleeping, Driving, Watching TV Single user Dataset (2022) 43 Gyroscope, GPS, Microphone DOO-RE (2024) Brightness, Humidity, Temperature, Eating, Reading, Phone call, Seminar, Single user, Sound, Podium, Door, Motion, Seat, Lab meeting, Small talk, Studying together, Group user Aircon, Light, Projector Technical discussion, Eating together The existing datasets focus on personal spaces, such as smart homes, which consist of a small number of users and where only simple activities occur. Details of each dataset can be found in the corresponding citing paper.…”

Section: Background and Summarymentioning

confidence: 99%

A dataset of ambient sensors in a meeting room for activity recognition

Kim,

Lee

et al. 2024

Sci Data

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As IoT technology advances, using machine learning to detect user activities emerges as a promising strategy for delivering a variety of smart services. It is essential to have access to high-quality data that also respects privacy concerns and data streams from ambient sensors in the surrounding environment meet this requirement. However, despite growing interest in research, there is a noticeable lack of datasets from ambient sensors designed for public spaces, as opposed to those for private settings. To bridge this gap, we design the DOO-RE dataset within an actual meeting room environment, equipped with three types of ambient sensors: those triggered by actuators, users, and the environment itself. This dataset is compiled from the activities of over twenty students throughout a period of four months. DOO-RE provides reliable and purpose-oriented activity data in a public setting, with activity labels verified by multiple annotators through a process of cross-validation to guarantee data integrity. DOO-RE categorizes nine different types of activities and facilitates the study of both single and group activities. We are optimistic that DOO-RE will play a significant role in advancing human activity recognition technologies, enhancing smart automation systems, and enabling the rapid setup of smart spaces through ambient sensors.

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Section: Background and Summarymentioning

confidence: 99%

A dataset of ambient sensors in a meeting room for activity recognition

Kim,

Lee

et al. 2024

Sci Data

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“…Different smart devices (e.g., Fitbit, Garmin, Smartwatches, Sensewear Mini Armband, My Wellness Key Accelerometer, Actigraph, Pedometer, smartphone with installed applications) are available in the market to monitor and track fitness-related metrics (e.g., steps, VPA, MPA, low physical activity (LPA), sedentary bouts, calorie burnt, distance covered via running or walking) and related vital health signs (e.g., heart rate variability, respiratory rate, heart rate). The collected activity data is often available preprocessed (e.g., PMData 5 , Zenodo activity data 6 ) or raw (e.g., UCI-HAR, WISDM, SHL, MD, HARTH, and AlgoSnap) 7 . Such data is seen as very important in the scientific research community.…”

Section: Overviewmentioning

confidence: 99%

“…Such data is seen as very important in the scientific research community. Several researchers have explored the use of sensors available in mobile devices to identify stationary activities for further applications in different scenarios related to ambient assisted living (AAL) and augmented living environments (ALE) 7 . Prior to this point, there has been a scarcity of openly accessible datasets capturing physical activity data using the MOX2-5 activity monitoring medical-grade (CE certified) device.…”

Section: Overviewmentioning

confidence: 99%

Semantic representation and comparative analysis of physical activity sensor observations using MOX2-5 sensor in real and synthetic datasets: a proof-of-concept-study

Chatterjee,

Gerdes,

Prinz

et al. 2024

Sci Rep

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The widespread use of devices like mobile phones and wearables allows for automatic monitoring of human daily activities, generating vast datasets that offer insights into long-term human behavior. A structured and controlled data collection process is essential to unlock the full potential of this information. While wearable sensors for physical activity monitoring have gained significant traction in healthcare, sports science, and fitness applications, securing diverse and comprehensive datasets for research and algorithm development poses a notable challenge. In this proof-of-concept study, we underscore the significance of semantic representation in enhancing data interoperability and facilitating advanced analytics for physical activity sensor observations. Our approach focuses on enhancing the usability of physical activity datasets by employing a medical-grade (CE certified) sensor to generate synthetic datasets. Additionally, we provide insights into ethical considerations related to synthetic datasets. The study conducts a comparative analysis between real and synthetic activity datasets, assessing their effectiveness in mitigating model bias and promoting fairness in predictive analysis. We have created an ontology for semantically representing observations from physical activity sensors and conducted predictive analysis on data collected using MOX2-5 activity sensors. Until now, there has been a lack of publicly available datasets for physical activity collected with MOX2-5 activity monitoring medical grade (CE certified) device. The MOX2-5 captures and transmits high-resolution data, including activity intensity, weight-bearing, sedentary, standing, low, moderate, and vigorous physical activity, as well as steps per minute. Our dataset consists of physical activity data collected from 16 adults (Male: 12; Female: 4) over a period of 30–45 days (approximately 1.5 months), yielding a relatively small volume of 539 records. To address this limitation, we employ various synthetic data generation methods, such as Gaussian Capula (GC), Conditional Tabular General Adversarial Network (CTGAN), and Tabular General Adversarial Network (TABGAN), to augment the dataset with synthetic data. For both the authentic and synthetic datasets, we have developed a Multilayer Perceptron (MLP) classification model for accurately classifying daily physical activity levels. The findings underscore the effectiveness of semantic ontology in semantic search, knowledge representation, data integration, reasoning, and capturing meaningful relationships between data. The analysis supports the hypothesis that the efficiency of predictive models improves as the volume of additional synthetic training data increases. Ontology and Generative AI hold the potential to expedite advancements in behavioral monitoring research. The data presented, encompassing both real MOX2-5 and its synthetic counterpart, serves as a valuable resource for developing robust methods in activity type classification. Furthermore, it opens avenues for exploration into research directions related to synthetic data, including model efficiency, detection of generated data, and considerations regarding data privacy.

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“…Different smart devices (e.g., Fitbit, Garmin, Smartwatches, Sensewear Mini Armband, My Wellness Key Accelerometer, Actigraph, Pedometer, smartphone with installed applications) are available in the market to monitor and track fitness-related metrics (e.g., steps, VPA, MPA, low physical activity (LPA), sedentary bouts, calorie burnt, distance covered via running or walking) and related vital health signs (e.g., heart rate variability, respiratory rate, heart rate). The collected activity data is often available preprocessed (e.g., PMData [5], Zenodo activity data [6]) or raw (e.g., UCI-HAR, WISDM, SHL, MD, HARTH, and AlgoSnap) [7]. Such data is seen as very important in the scientific research community.…”

Section: Overviewmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

Semantic Representation of Physical Activity Sensor Observations and Comparative Analysis of Real and Synthetic Datasets: A Proof-of-Concept-Study with MOX2-5 Sensor

Chatterjee,

Gerdes,

Prinz

et al. 2023

Preprint

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Background Daily activity of humans is monitored at a large scale automatically by devices such as mobile phones and wearables. This produces immense amounts of data that can be used to get a better understanding of human behavior over time. To understand this data and its possibilities, a structured and controlled collection process is required. Physical activity monitoring using wearable sensors has attracted prevalent attention in healthcare, sports science, and fitness applications. However, ensuring the availability of diverse and comprehensive datasets for research and algorithm development can be challenging. Objective We emphasize the importance of semantic representation for physical activity sensor observations to enable data interoperability and advanced analytics. In this proof-of-concept study, we propose an approach to improve the usability of physical activity datasets and highlight ethical considerations by generating synthetic datasets using medical-grade (CE certified) sensor. Moreover, our study presents a comparative analysis between real and synthetic activity datasets, evaluating their utilities to address model bias and fairness in predictive analysis. Methods We design and develop an ontology for semantic representation of physical activity sensor observations and predictive analysis on collected data with MOX2-5 activity sensors. The MOX2-5 activity monitoring device can collect and transmit high-resolution activity data such as activity intensity, weight-bearing, sedentary, standing, low physical activity, moderate physical activity, vigorous physical activity, and steps per minute. We collected physical activity data from 16 adults (Male: 12; Female: 4) for 30–45 days (about 1 and a half months). It produced a volume of 539 records which is small. Thus, we utilize different synthetic data generation methods, such as Gaussian Capula (GC), Conditional Tabular General Adversarial Network (CTGAN), and Tabular General Adversarial Network (TABGAN) to enhance the dataset with synthetic data. For both the real and synthetic datasets, we developed a Multilayer Perceptron (MLP) classification model to classify daily physical activity levels. Results The results highlight that semantic ontology is suitable for semantic search, knowledge representation, data integration, reasoning, and capturing the meaning and relationships between data. The analysis proves the hypothesis that the efficiency of predictive models grows with the increasing volume of additional synthetic training data. Conclusions The potential of ontology and Generative AI may accelerate research and innovation in the field of behavioral monitoring. Moreover, the presented data (both real MOX2-5 and its synthetic version) will be helpful in the creation of robust methods for the classification of activity types and different research directions in connection to synthetic data such as model efficiency, detection of generated data and data privacy.

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Daily motionless activities: A dataset with accelerometer, magnetometer, gyroscope, environment, and GPS data

Cited by 7 publications

References 19 publications

A dataset of ambient sensors in a meeting room for activity recognition

A dataset of ambient sensors in a meeting room for activity recognition

Semantic representation and comparative analysis of physical activity sensor observations using MOX2-5 sensor in real and synthetic datasets: a proof-of-concept-study

Semantic Representation of Physical Activity Sensor Observations and Comparative Analysis of Real and Synthetic Datasets: A Proof-of-Concept-Study with MOX2-5 Sensor

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