This paper lays the grounds for a new methodology for detecting thermal discomfort, which can potentially reduce the building energy usage while improving the comfort of its inhabitants. The paper describes our explorations in automatic human discomfort prediction using physiological signals directly collected from a buildings inhabitants. Using infrared thermography, as well as several other bio-sensors (galvanic skin response, heart rate tracker, respiration rate tracker), we record a building’s inhabitants under various thermal conditions (hot, cold, neutral), and consequently build a multimodal model that can automatically detect thermal discomfort.The paper makes two important contributions. First, we introduce a novel dataset, consisting of sensorial measurements of human behavior under varied comfort/discomfort conditions. The change in physiological signals of the human body are monitored for several subjects, for different comfort levels in an indoor environment. Second, using the dataset obtained in the first step, we build a model that identifies the relationship between human factors, as tracked through infrared thermography and other bio-sensors, and environmental conditions related to discomfort. Third, we measure the correlation between sensorial measurements collected from the user and self-reported levels of discomfort, and hence identify the sensorial measurements that are predictive of human discomfort. The final goal is to automatically predict the level of discomfort of a building inhabitant without any explicit input from the user.This human-centered discomfort prediction model is expected to enable innovative adaptive control scenarios for a built environment conditions in real time, as well as a significant reduction in building energy usage directly related to human occupancy and their desired comfort levels.Copyright © 2014 by ASME

Using Infrared Thermography and Biosensors to Detect Thermal Discomfort in a Building’s Inhabitants

Current studies of outdoor thermal comfort are limited to calculating thermal indices or interviewing people. The first method does not take into account the way people use this space, whereas the second one is limited to one particular study area. Simulating people’s thermal perception along with their activities in public urban spaces will help architects and city planners to test their concepts and to design smarter and more liveable cities. In this paper, we propose an agent-based modelling approach to simulate people’s adaptive behaviour in space. Two levels of pedestrian behaviour are considered: reactive and proactive, and three types of thermal adaptive behaviour of pedestrians are modelled with single-agent scenarios: speed adaptation, thermal attraction/repulsion and vision-motivated route alternation. An "accumulated heat stress" parameter of the agent is calculated during the simulation, and pedestrian behaviour is analysed in terms of its ability to reduce the accumulated heat stress. This work is the first step towards the "human component" in urban microclimate simulation systems. We use these simulations to drive the design of real-life experiments, which will help calibrating model parameters, such as the heat-speed response, thermal sensitivity and admissible turning angles.

/pdf/models-of-pedestrian-adaptive-behaviour-in-hot-outdoor-2iqo8p13qk.pdf

Models of Pedestrian Adaptive Behaviour in Hot Outdoor Public Spaces

Most of the applications for pedestrian navigation are based on information that is useful for car navigation systems, while the basic needs of pedestrians are seldom considered. We contribute to the current research in the field by proposing a pedestrian-tailored navigation application based on path attributes, that as of yet have not been considered in a pedestrian route calculation. We focus in particular on shaded paths, which are considered preferable during hot, sunny days, especially by pedestrians of advanced age, those with small children or persons that suffer from sensitivity to light. Evaluation results regarding the performance of the application provided a significantly better acceptance rate by the end users in terms of light intensity, amount of shade and positive affect that was derived from walking through the suggested routes.

Shadow as Route Quality Parameter in a Pedestrian-Tailored Mobile Application

Pedestrian Navigation Applications (PNAs) provide assistance in terms of self-localization, space recognition, and turn-by-turn navigation. The use, motivations and perceptions associated with these applications have been under investigated due to users being insufficiently involved in their design and development. This paper analyses the extent to which PNAs are used for assisting people to walk, the frequencies and reasons of using these applications, the perceptions about them, and the barriers preventing them from being used. The study is supported by a questionnaire (N = 1438) that was administered in Bologna (Italy) and Porto (Portugal). Results indicated that 42% of the respondents use PNAs mainly on an occasional basis to find locations and the shortest routes. Google Maps was the preferred navigation service. Statistical tests showed that PNAs were more likely to be used by younger adults and students. The lack of need was the main reason for not using these apps, due to the good spatial knowledge of the cities or the non-use of the pedestrian mode for regular trips. Respondents would like to have apps that are more accurate, usable, and adjusted to pedestrian navigation. The findings described in this paper could be helpful for future designs of PNAs, especially to match pedestrian needs more effectively and to enhance the role of these apps in promoting healthier and sustainable lifestyles.

https://repositorium.sdum.uminho.pt/bitstream/1822/74089/1/3272-ijgi-10-00446%20%281%29.pdf

Use and Perceptions of Pedestrian Navigation Apps: Findings from Bologna and Porto

This paper provides a new approach to the automatic detection of thermal discomfort. We see this research as a step toward the development of an intelligent climate control system that does not require any explicit input from the users. We introduce a novel dataset that simulates different thermal comfort/discomfort levels and we provide a complete analysis of different physiological signals and their capability of discriminating between these levels. Our approach is successful in detecting the thermal sensation of human subjects and it is expected to enable innovative adaptive control scenarios for enclosed environments as well as a significant reduction in energy consumption.

Detecting Human Thermal Discomfort via Physiological Signals

A framework for pedestrian comfort navigation using multi-modal environmental sensors

Pedestrian dead reckoning plays an important role in indoor pedestrian localization applications. Although this approach has a notable advantage that no extra infrastructure is required, it also suffers an issue known as the drift, which means the estimation errors accumulate and ultimately may make the result unreliable. In this paper, we propose a circular inference method applying online learning in order to reduce such drift errors. Map information is used as prior knowledge and identified land marks are used as triggers for learning processing. A multidimensional optimization algorithm is designed and used in learning phase to efficiently tune the estimation parameters. On the basis of the design we implement an end system running on smartphones and use it in the evaluation experiments. The results show that the proposed method can effectively improve the accuracy and reliability of the localization system.

DECL: A circular inference method for indoor pedestrian localization using phone inertial sensors

In this paper, we propose a novel sparse particle filter applied for indoor pedestrian localization. Unlike the traditional particle filters that usually contain thousands of particles, this algorithm uses one or several particle sequences in location tracking. The particle is split to cover all the possible tracks when matching ambiguity occurs. This algorithm can significantly reduce the computation burden as well as energy consumption. Field experiments are conducted using mobile phones and the results show that the proposed method can perform localization with much higher efficiency, high reliability, and merely slight loss of accuracy.

A sparse particle filter for indoor localization using mobile phones

In this paper, we present an integrated framework, named NaviComf, which constructs pedestrian navigation systems for comfort in varying environments by using multi-modal sensing technologies. With NaviComf we aim to systematically provide solutions to three key problems: (1) how to build the environmental data warehouse (EDW) which works as an infrastructure providing comprehensive and predictive environmental information, (2) how to integrate heterogeneous environmental information from multi-modal sensors into an aggregate value which facilitates further processing, and (3) how to determine the optimal path plans in environments which are varying continuously. In NaviComf the multidimensional data model and data prediction method are applied to build the EDW. Then a novel multi-factor cost (MFC) model is proposed as the fundamental concept to integrate the multi-modal sensor data. Based on the former two solutions, the optimal path planning (PP) problem is solved in a time-dependent network by applying a dynamic programming method. In the evaluations of NaviComf, sensor data for temperature, humidity, and pedestrian traffic flow have been gathered in real environments and a prototype system has been implemented with the data. Evaluations are conducted by using the prototype system and the results show that NaviComf can efficiently navigate pedestrians through more comfortable paths as compared to the traditional navigation method.

NaviComf: Navigate pedestrians for comfort using multi-modal environmental sensors

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Papers

A framework for pedestrian comfort navigation using multi-modal environmental sensors

DECL: A circular inference method for indoor pedestrian localization using phone inertial sensors

A sparse particle filter for indoor localization using mobile phones

NaviComf: Navigate pedestrians for comfort using multi-modal environmental sensors