Exploring Indoor Health: An In-depth Field Study on the Indoor Air Quality Dynamics

Indoor air pollution, a significant driver of respiratory and cardiovascular diseases, claims 3.2 million lives yearly, according to the World Health Organization, highlighting the pressing need to address this global crisis. In contrast to unconstrained outdoor environments, room structures, floor plans, ventilation systems, and occupant activities all impact the accumulation and spread of pollutants. Yet, comprehensive in-the-wild empirical studies exploring these unique indoor air pollution patterns and scope are lacking. To address this, we conducted a three-month-long field study involving over 28 indoor spaces to delve into the complexities of indoor air pollution. Our study was conducted using our custom-built DALTON air quality sensor and monitoring system, an innovative IoT air quality monitoring solution that considers cost, sensor type, accuracy, network connectivity, power, and usability. Our study also revealed that conventional measures, such as the Indoor Air Quality Index (IAQI), don't fully capture complex indoor air quality dynamics. Hence, we proposed the Healthy Home Index (HHI), a new metric considering the context and household activities, offering a more comprehensive understanding of indoor air quality. Our findings suggest that HHI provides a more accurate air quality assessment, underscoring the potential for wide-scale deployment of our indoor air quality monitoring platform.Comment: 15 pages, 19 figure

AmicroN: A Framework for Generating Annotations for Human Activity Recognition with Granular Micro-Activities

Efficient human activity recognition (HAR) using sensor data needs a significant volume of annotated data. The growing volume of unlabelled sensor data has challenged conventional practices for gathering HAR annotations with human-in-the-loop approaches, often leading to the collection of shallower annotations. These shallower annotations ignore the fine-grained micro-activities that constitute any complex activities of daily living (ADL). Understanding this, we, in this paper, first analyze this lack of granular annotations from available pre-annotated datasets to understand the practical inconsistencies and also perform a detailed survey to look into the human perception surrounding annotations. Drawing motivations from these, we next develop the framework AmicroN that can automatically generate micro-activity annotations using locomotive signatures and the available coarse-grain macro-activity labels. In the backend, AmicroN applies change-point detection followed by zero-shot learning with activity embeddings to identify the unseen micro-activities in an unsupervised manner. Rigorous evaluation on publicly available datasets shows that AmicroN can accurately generate micro-activity annotations with a median F1-score of >0.75. Additionally, we also show that AmicroN can be used in a plug-and-play manner with Large Language Models (LLMs) to obtain the micro-activity labels, thus making it more practical for realistic applications.Comment: 27 pages, 5 tables, 9 figure

Restructuring in Telecommunications and its Market Impacts: An Event-Study Analysis

The aim of the study is to present a detailed assessment of the major strategic changes in the telecommunications industry and their impact on market returns. Using a global sample of major telecom companies, significant events related to restructuring were identified and then assessed in terms of their favorable or unfavorable impacts. Based on the methodology of event-study analysis with GARCH specification, the impact of events was tested after incorporating dummy variables of different lengths (7, 15 and 20). Simultaneously, subsequent to a regression, Cumulative Abnormal Returns (CAR) were calculated within a variable event window of 7, 15 and 20 Days. Market returns were studied starting from 1996 until 2008. The results show interesting patterns in terms of how the market views restructuring in the business model of telecom companies, organizational structure, alliances and mergers, and technological platform changes. Countries differ significantly in how they view telecoms restructuring and what changes are considered beneficial by investor and which ones are not