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Connecting personal-scale sensing and networked community behavior to infer human activities

Published: 13 September 2014 Publication History

Abstract

Advances in mobile and wearable devices are making it feasible to deploy sensing systems at a large-scale. However, slower progress is being made in activity recognition which remains often unreliable in everyday environments. In this paper, we investigate how to leverage the increasing capacity to gather data at a population-scale towards improving existing models of human behavior. Specifically, we consider the various social phenomena and environmental factors that cause people to develop correlated behavioral patterns, especially within communities connected by strong social ties. Reasons underpinning correlated behavior include shared externalities (e.g., work schedules, weather, traffic conditions), that shape options and decisions; and cases of adopted behavior, as people learn from each other or assume group norms due to social pressure. Most existing approaches to modeling human behavior ignore all of these phenomena and recognize activities solely on the basis of sensor data captured from a single individual. We propose the Networked Community Behavior (NCB) framework for activity recognition, specifically designed to exploit community-scale behavioral patterns. Under NCB, patterns of community behavior are mined to identify social ties that can signal correlated behavior, this information is used to augment sensor-based inferences available from the actions of individuals. Our evaluation of NCB shows it is able to outperform existing approaches to behavior modeling across four mobile sensing datasets that collectively require a diverse set of activities to be recognized.

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      cover image ACM Conferences
      UbiComp '14: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing
      September 2014
      973 pages
      ISBN:9781450329682
      DOI:10.1145/2632048
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 13 September 2014

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      Author Tags

      1. activity recognition
      2. community learning
      3. mobile sensing

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      UbiComp '14
      UbiComp '14: The 2014 ACM Conference on Ubiquitous Computing
      September 13 - 17, 2014
      Washington, Seattle

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      Overall Acceptance Rate 764 of 2,912 submissions, 26%

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      Cited By

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      • (2023)Two-Domain Joint Attention Mechanism Based on Sensor Data for Group Activity RecognitionIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2023.324646972(1-15)Online publication date: 2023
      • (2021)GoldenTime: Exploring System-Driven Timeboxing and Micro-Financial Incentives for Self-Regulated Phone UseProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445489(1-17)Online publication date: 6-May-2021
      • (2021)Tracking and Behavior Augmented Activity Recognition for Multiple InhabitantsIEEE Transactions on Mobile Computing10.1109/TMC.2019.293638220:1(247-262)Online publication date: 1-Jan-2021
      • (2020)NADAL: A Neighbor-Aware Deep Learning Approach for Inferring Interpersonal Trust Using Smartphone DataComputers10.3390/computers1001000310:1(3)Online publication date: 24-Dec-2020
      • (2020)Exceptional spatio-temporal behavior mining through Bayesian non-parametric modelingData Mining and Knowledge Discovery10.1007/s10618-020-00674-zOnline publication date: 29-Jan-2020
      • (2019)PocketCareProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33289123:2(1-23)Online publication date: 21-Jun-2019
      • (2019)GEVRProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33144213:1(1-25)Online publication date: 29-Mar-2019
      • (2017)Capturing Daily Student Life by Recognizing Complex Activities Using SmartphonesProceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3144457.3144472(156-165)Online publication date: 7-Nov-2017
      • (2017)Unseen Activity Recognitions: A Hierarchical Active Transfer Learning Approach2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS.2017.264(436-446)Online publication date: Jun-2017
      • (2016)CACE: Exploiting Behavioral Interactions for Improved Activity Recognition in Multi-inhabitant Smart Homes2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS.2016.61(539-548)Online publication date: Jun-2016
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