“…When estimating both I cl and M, refs. [34,35] use a CNN (Convolutional Neural Network)-based classifier to recognize a person's clothes type and activity type, and then refer ISO (International Organization for Standardization) standard tables to get the I cl and M values from the recognized types. These works prove the importance of clothing status (short sleeves, long sleeves) and posture (sitting, standing) in estimating I cl and M. However, refs.…”
Section: CL and M Estimationmentioning
confidence: 99%
“…These works prove the importance of clothing status (short sleeves, long sleeves) and posture (sitting, standing) in estimating I cl and M. However, refs. [34,35] are only valid in a simple and controlled single-person environment. Expanding and enriching this kind of solution is in great need.…”
Section: CL and M Estimationmentioning
confidence: 99%
“…To integrate clothing status and key posture recognition into this detection procedure, we classify persons into six categories (see Table 3). Here the clothing status is represented by the sleeve status (long, short) for three reasons: (i) these two are the most common clothing situations in an office environment while the lower part of the body is often totally occluded by the desk; (ii) according to to [10,34,35], sleeve status is significantly important in estimating I cl ; (iii) the change between a long-sleeved status to a short-sleeved status by rolling up sleeves or taking off outer jackets is a sign of feeling hot and vice versa, indicating a person's thermal sensation directly; (iv) the sleeves status helps to locate skin region and clothes region separately for further skin and clothes temperatures acquisition. For example, the elbows of a person wearing short-sleeved clothes are skin regions, while the elbows of a person wearing long-sleeved clothes are clothes regions.…”
Section: Tracking-by-detectionmentioning
confidence: 99%
“…For example, the elbows of a person wearing short-sleeved clothes are skin regions, while the elbows of a person wearing long-sleeved clothes are clothes regions. This localization makes it possible to use such key body points to calculate a person's skin temperature and clothes temperature, because key body points on arms are widely used sensitive heat receptors in thermal comfort assessment [35,[54][55][56]. Besides the two statuses of long sleeves and short sleeves, another status called difficult to predict clothes type due to occlusion is also usual in daily life.…”
Section: Tracking-by-detectionmentioning
confidence: 99%
“…After considering such situations, this research counts the lower arms (the middle point of the elbow and wrist) for short-sleeved clothes and the nose area as R s , and the elbows for long-sleeved clothes and the shoulders as R c . These regions are also widely used heat receptors in thermal comfort research [35,[54][55][56]. Figure 3 illustrates R s in green crosses and R c in red crosses on four images.…”
Satisfactory indoor thermal environments can improve working efficiencies of office staff. To build such satisfactory indoor microclimates, individual thermal comfort assessment is important, for which personal clothing insulation rate (Icl) and metabolic rate (M) need to be estimated dynamically. Therefore, this paper proposes a vision-based method. Specifically, a human tracking-by-detection framework is implemented to acquire each person’s clothing status (short-sleeved, long-sleeved), key posture (sitting, standing), and bounding box information simultaneously. The clothing status together with a key body points detector locate the person’s skin region and clothes region, allowing the measurement of skin temperature (Ts) and clothes temperature (Tc), and realizing the calculation of Icl from Ts and Tc. The key posture and the bounding box change across time can category the person’s activity intensity into a corresponding level, from which the M value is estimated. Moreover, we have collected a multi-person thermal dataset to evaluate the method. The tracking-by-detection framework achieves a mAP50 (Mean Average Precision) rate of 89.1% and a MOTA (Multiple Object Tracking Accuracy) rate of 99.5%. The Icl estimation module gets an accuracy of 96.2% in locating skin and clothes. The M estimation module obtains a classification rate of 95.6% in categorizing activity level. All of these prove the usefulness of the proposed method in a multi-person scenario of real-life applications.
“…When estimating both I cl and M, refs. [34,35] use a CNN (Convolutional Neural Network)-based classifier to recognize a person's clothes type and activity type, and then refer ISO (International Organization for Standardization) standard tables to get the I cl and M values from the recognized types. These works prove the importance of clothing status (short sleeves, long sleeves) and posture (sitting, standing) in estimating I cl and M. However, refs.…”
Section: CL and M Estimationmentioning
confidence: 99%
“…These works prove the importance of clothing status (short sleeves, long sleeves) and posture (sitting, standing) in estimating I cl and M. However, refs. [34,35] are only valid in a simple and controlled single-person environment. Expanding and enriching this kind of solution is in great need.…”
Section: CL and M Estimationmentioning
confidence: 99%
“…To integrate clothing status and key posture recognition into this detection procedure, we classify persons into six categories (see Table 3). Here the clothing status is represented by the sleeve status (long, short) for three reasons: (i) these two are the most common clothing situations in an office environment while the lower part of the body is often totally occluded by the desk; (ii) according to to [10,34,35], sleeve status is significantly important in estimating I cl ; (iii) the change between a long-sleeved status to a short-sleeved status by rolling up sleeves or taking off outer jackets is a sign of feeling hot and vice versa, indicating a person's thermal sensation directly; (iv) the sleeves status helps to locate skin region and clothes region separately for further skin and clothes temperatures acquisition. For example, the elbows of a person wearing short-sleeved clothes are skin regions, while the elbows of a person wearing long-sleeved clothes are clothes regions.…”
Section: Tracking-by-detectionmentioning
confidence: 99%
“…For example, the elbows of a person wearing short-sleeved clothes are skin regions, while the elbows of a person wearing long-sleeved clothes are clothes regions. This localization makes it possible to use such key body points to calculate a person's skin temperature and clothes temperature, because key body points on arms are widely used sensitive heat receptors in thermal comfort assessment [35,[54][55][56]. Besides the two statuses of long sleeves and short sleeves, another status called difficult to predict clothes type due to occlusion is also usual in daily life.…”
Section: Tracking-by-detectionmentioning
confidence: 99%
“…After considering such situations, this research counts the lower arms (the middle point of the elbow and wrist) for short-sleeved clothes and the nose area as R s , and the elbows for long-sleeved clothes and the shoulders as R c . These regions are also widely used heat receptors in thermal comfort research [35,[54][55][56]. Figure 3 illustrates R s in green crosses and R c in red crosses on four images.…”
Satisfactory indoor thermal environments can improve working efficiencies of office staff. To build such satisfactory indoor microclimates, individual thermal comfort assessment is important, for which personal clothing insulation rate (Icl) and metabolic rate (M) need to be estimated dynamically. Therefore, this paper proposes a vision-based method. Specifically, a human tracking-by-detection framework is implemented to acquire each person’s clothing status (short-sleeved, long-sleeved), key posture (sitting, standing), and bounding box information simultaneously. The clothing status together with a key body points detector locate the person’s skin region and clothes region, allowing the measurement of skin temperature (Ts) and clothes temperature (Tc), and realizing the calculation of Icl from Ts and Tc. The key posture and the bounding box change across time can category the person’s activity intensity into a corresponding level, from which the M value is estimated. Moreover, we have collected a multi-person thermal dataset to evaluate the method. The tracking-by-detection framework achieves a mAP50 (Mean Average Precision) rate of 89.1% and a MOTA (Multiple Object Tracking Accuracy) rate of 99.5%. The Icl estimation module gets an accuracy of 96.2% in locating skin and clothes. The M estimation module obtains a classification rate of 95.6% in categorizing activity level. All of these prove the usefulness of the proposed method in a multi-person scenario of real-life applications.
Since indoor clothing insulation is a key element in thermal comfort models, the aim of the present study is proposing an approach for predicting it, which could assist the occupants of a building in terms of recommendations regarding their ensemble. For that, a systematic analysis of input variables is exposed, and 13 regression and 12 classification machine learning algorithms were developed and compared. The results are based on data from 3352 questionnaires and 21 input variables from a field study in mixed-mode office buildings in Spain. Outdoor temperature at 6 a.m., indoor air temperature, indoor relative humidity, comfort temperature and gender were the most relevant features for predicting clothing insulation. When comparing machine learning algorithms, decision tree-based algorithms with Boosting techniques achieved the best performance. The proposed model provides an efficient method for forecasting the clothing insulation level and its application would entail optimising thermal comfort and energy efficiency.
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