Real Time Measurement of Dynamic Metabolic Factor (D-MET)

Dziedzic, Jakub; Yan, Da; Novaković, Vojislav

doi:10.1007/978-3-030-00662-4_57

Cited by 2 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To study humans' health and wellbeing in indoor environments their activities and metabolic rate are too highly relevant types of information. As an example, Dziedzic et al [10] have proposed an estimation model using the movement skeleton joints of the human body captured by a Microsoft Kinect installed in a household. The skeleton movement data is used to estimate an occupant's metabolic rate and activities.…”

Section: Analysis Of Domain Casesmentioning

confidence: 99%

See 1 more Smart Citation

Ontology-Based Modeling of Privacy Vulnerabilities for Data Sharing

Schwee

Sangogboye

Johansen

et al. 2020

Privacy and Identity Management. Data for Better Living: AI and Privacy

View full text Add to dashboard Cite

When several parties want to share sensor-based datasets it can be difficult to know exactly what kinds of information can be extracted from the shared data. This is because many types of sensor data can be used to estimate indirect information, e.g., in smart buildings a CO 2 stream can be used to estimate the presence and number of occupants in each room. If a data publisher does not consider these transformations of data their privacy protection of the data might be problematic. It currently requires a manual inspection by a knowledge expert of each dataset to identify possible privacy vulnerabilities for estimating indirect information. This manual process does not scale with the increasing availability of data due to the general lack of experts and the associated cost with their work. To improve this process, we propose a privacy vulnerability ontology that helps highlight the specific privacy challenges that can emerge when sharing a dataset. The ontology is intended to model data transformations, privacy attacks, and privacy risks regarding data streams. In the paper, we have used the ontology for modeling the findings of eight papers in the smart building domain. Furthermore, the ontology is applied to a case study scenario using a published dataset. The results show that the ontology can be used to highlight privacy risks in datasets.

show abstract

Section: Analysis Of Domain Casesmentioning

confidence: 99%

“…The instance for the paper [10], can be seen in Figure 9. In this instance, it can be observed that weight and metabolic rates for the monitored individual can be estimated by fusing body skeleton joint data with external data.…”

Section: Models Of Domain Casesmentioning

confidence: 99%

Ontology-Based Modeling of Privacy Vulnerabilities for Data Sharing

Schwee

Sangogboye

Johansen

et al. 2020

Privacy and Identity Management. Data for Better Living: AI and Privacy

View full text Add to dashboard Cite

show abstract

“…A total of 12 poses of thermal comfort were defined and Kinect was adopted to estimate human skeleton and poses. Further, Dziedzic [23] also used Kinect to predict human thermal sensation and dynamic metabolic rate.…”

Section: Related Workmentioning

confidence: 99%

“…Many methods were generated, including the questionnaire survey method [3][4][5], environmental measurement method [6,7] and invasive measuring method of human body physiological parameters [8][9][10][11][12][13][14][15][16][17][18]. In recent years, semi-invasive measuring method [19,20] and non-invasive measuring method [21][22][23] for human body physiological parameters were also generated. For example, in [19,20], an infrared sensor was fixed on the frame of eyeglasses in order to measure skin temperature.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Contactless Measuring Method of Skin Temperature based on the Skin Sensitivity Index and Deep Learning

et al. 2019

View full text Add to dashboard Cite

Featured Application: The NISDL method proposed in this paper can be used for real time noninvasively measuring human skin temperature, which reflect human body thermal comfort status and can be used for control HVAC devices.Abstract: In human-centered intelligent building, real-time measurements of human thermal comfort play critical roles and supply feedback control signals for building heating, ventilation, and air conditioning (HVAC) systems. Due to the challenges of intra-and inter-individual differences and skin subtleness variations, there is no satisfactory solution for thermal comfort measurements until now. In this paper, a non-invasive measuring method based on skin sensitivity index and deep learning (NISDL) was proposed to measure real-time skin temperature. A new evaluating index, named skin sensitivity index (SSI), was defined to overcome individual differences and skin subtleness variations. To illustrate the effectiveness of SSI proposed, two multi-layers deep learning framework (NISDL method I and II) was designed and the DenseNet201 was used for extracting features from skin images. The partly personal saturation temperature (NIPST) algorithm was use for algorithm comparisons. Another deep learning algorithm without SSI (DL) was also generated for algorithm comparisons. Finally, a total of 1.44 million image data was used for algorithm validation. The results show that 55.6180% and 52.2472% error values (NISDL method I, II) are scattered at [0 °C, 0.25 °C), and the same error intervals distribution of NIPST is 35.3933%.

show abstract

Real Time Measurement of Dynamic Metabolic Factor (D-MET)

Cited by 2 publications

References 6 publications

Ontology-Based Modeling of Privacy Vulnerabilities for Data Sharing

Ontology-Based Modeling of Privacy Vulnerabilities for Data Sharing

A Contactless Measuring Method of Skin Temperature based on the Skin Sensitivity Index and Deep Learning

Contact Info

Product

Resources

About