Unsupervised activity recognition using automatically mined common sense

A fundamental difficulty in recognizing human activities is obtaining the labeled data needed to learn models of those activities. Given emerging sensor technology, however, it is possible to view activity data as a stream of natural language terms. Activity models are then mappings from such terms to activity names, and may be extracted from text corpora such as the web. We show that models so extracted are sufficient to automatically produce labeled segmentations of activity data with an accuracy of 42 % over 26 activities, well above the 3.8 % baseline. The segmentation so obtained is sufficient to bootstrap learning, with accuracy of learned models increasing to 52%. To our knowledge, this is the first human activity inferencing system shown to learn from sensed activity data with no human intervention per activity learned, even for labeling

Sensing and modeling human networks

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2004.Includes bibliographical references (p. 101-105).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Knowledge of how groups of people interact is important in many disciplines, e.g. organizational behavior, social network analysis, knowledge management and ubiquitous computing. Existing studies of social network interactions have either been restricted to online communities, where unambiguous measurements about how people interact can be obtained (available from chat and email logs), or have been forced to rely on questionnaires, surveys or diaries to get data on face-to-face interactions between people. The aim of this thesis is to automatically model face-to-face interactions within a community. The first challenge was to collect rich and unbiased sensor data of natural interactions. The "sociometer", a specially designed wearable sensor package, was built to address this problem by unobtrusively measuring face-to-face interactions between people. Using the sociometers, 1518 hours of wearable sensor data from 23 individuals was collected over a two-week period (66 hours per person). This thesis develops a computational framework for learning the interaction structure and dynamics automatically from the sociometer data. Low-level sensor data are transformed into measures that can be used to learn socially relevant aspects of people's interactions - e.g. identifying when people are talking and whom they are talking to. The network structure is learned from the patterns of communication among people. The dynamics of a person's interactions, and how one person's dynamics affects the other's style of interaction are also modeled. Finally, a person's style of interaction is related to the person's role within the network. The algorithms are evaluated by comparing the output against hand-labeled and survey data.by Tanzeem Khalid Choudhury.Ph.D

Feasibility and Acceptability of Mobile Phone–Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults

Background: Chronic pain is a globally prevalent condition. It is closely linked with psychological well-being, and it is often concomitant with anxiety, negative affect, and in some cases even depressive disorders. In the case of musculoskeletal chronic pain, frequent physical activity is beneficial. However, reluctance to engage in physical activity is common due to negative psychological associations (eg, fear) between movement and pain. It is known that encouragement, self-efficacy, and positive beliefs are effective to bolster physical activity. However, given that the majority of time is spent away from personnel who can give such encouragement, there is a great need for an automated ubiquitous solution. Objective: MyBehaviorCBP is a mobile phone app that uses machine learning on sensor-based and self-reported physical activity data to find routine behaviors and automatically generate physical activity recommendations that are similar to existing behaviors. Since the recommendations are based on routine behavior, they are likely to be perceived as familiar and therefore likely to be actualized even in the presence of negative beliefs. In this paper, we report the preliminary efficacy of MyBehaviorCBP based on a pilot trial on individuals with chronic back pain. Methods: A 5-week pilot study was conducted on people with chronic back pain (N=10). After a week long baseline period with no recommendations, participants received generic recommendations from an expert for 2 weeks, which served as the control condition. Then, in the next 2 weeks, MyBehaviorCBP recommendations were issued. An exit survey was conducted to compare acceptance toward the different forms of recommendations and map out future improvement opportunities. Results: In all, 90% (9/10) of participants felt positive about trying the MyBehaviorCBP recommendations, and no participant found the recommendations unhelpful. Several significant differences were observed in other outcome measures. Participants found MyBehaviorCBP recommendations easier to adopt compared to the control (βint=0.42, P<.001) on a 5-point Likert scale. The MyBehaviorCBP recommendations were actualized more (βint=0.46, P<.001) with an increase in approximately 5 minutes of further walking per day (βint=4.9 minutes, P=.02) compared to the control. For future improvement opportunities, participants wanted push notifications and adaptation for weather, pain level, or weekend/weekday. Conclusions: In the pilot study, MyBehaviorCBP’s automated approach was found to have positive effects. Specifically, the recommendations were actualized more, and perceived to be easier to follow. To the best of our knowledge, this is the first time an automated approach has achieved preliminary success to promote physical activity in a chronic pain context. Further studies are needed to examine MyBehaviorCBP’s efficacy on a larger cohort and over a longer period of time

Current Issues and Future Directions for Research Into Digital Behavior Change Interventions

This series of five papers plus an accompanying commentary provides a “state-of-the-art” overview of some of the most pressing issues in the field of research into digital behavior change interventions (DBCIs), highlighting the need and potential for conceptual and methodologic advances. The papers are the product of a process of international expert consensus building, supported by the United Kingdom’s Medical Research Council, the U.S. NIH’s Office for Behavioral and Social Sciences Research, and the Robert Wood Johnson Foundation. The papers are aimed at a broad readership, including those who develop, evaluate, use, and fund DBCIs for both research and practical purposes. The aim is to provide guidance as to: 1. how more effective and cost-effective DCBIs can be developed, how they should be assessed, and the scientific priorities that must be addressed to advance research in this field; 2. how DBCIs can be used to advance scientific understanding of human behavior and behavior change.</p

Leveraging Multi-Modal Sensing for Mobile Health: A Case Review in Chronic Pain

Active and passive mobile sensing has garnered much attention in recent years. In this paper, we focus on chronic pain measurement and management as a case application to exemplify the state of the art. We present a consolidated discussion on the leveraging of various sensing modalities along with modular server-side and on-device architectures required for this task. Modalities included are: activity monitoring from accelerometry and location sensing, audio analysis of speech, image processing for facial expressions as well as modern methods for effective patient self-reporting. We review examples that deliver actionable information to clinicians and patients while addressing privacy, usability, and computational constraints. We also discuss open challenges in the higher level inferencing of patient state and effective feedback with potential directions to address them. The methods and challenges presented here are also generalizable and relevant to a broad range of other applications in mobile sensing

CrossCheck:toward passive sensing and detection of mental health changes in people with schizophrenia

Early detection of mental health changes in individuals with serious mental illness is critical for effective intervention. CrossCheck is the first step towards the passive monitoring of mental health indicators in patients with schizophrenia and paves the way towards relapse prediction and early intervention. In this paper, we present initial results from an ongoing randomized control trial, where passive smartphone sensor data is collected from 21 outpatients with schizophrenia recently discharged from hospital over a period ranging from 2-8.5 months. Our results indicate that there are statistically significant associations between automatically tracked behavioral features related to sleep, mobility, conversations, smartphone usage and self-reported indicators of mental health in schizophrenia. Using these features we build inference models capable of accurately predicting aggregated scores of mental health indicators in schizophrenia with a mean error of 7.6% of the score range. Finally, we discuss results on the level of personalization that is needed to account for the known variations within people. We show that by leveraging knowledge from a population with schizophrenia, it is possible to train accurate personalized models that require fewer individual-specific data to quickly adapt to new user

Multioccupant Activity Recognition in Pervasive Smart Home Environments

been the center of lot of research for many years now. The aim is to recognize the sequence of actions by a specific person using sensor readings. Most of the research has been devoted to activity recognition of single occupants in the environment. However, living environments are usually inhabited by more than one person and possibly with pets. Hence, human activity recognition in the context of multi-occupancy is more general, but also more challenging. The difficulty comes from mainly two aspects: resident identification, known as data association, and diversity of human activities. The present survey paper provides an overview of existing approaches and current practices for activity recognition in multi-occupant smart homes. It presents the latest developments and highlights the open issues in this field

Administracja samorządowa i instytucje kultury w województwie podlaskim. Podstawy regionalnej polityki publicznej

The subject of the analysis in the article is the participation of the local government administration in running a cultural institution. The situation in the Podlaskie voivodship has been analyzed on a nationwide basis. The main problems focus on the principles and methods of financing cultural institutions by regional self-government and various ways of obtaining additional, extra-budgetary funding for their activities, especially from Ministry of Culture and National Heritage and the European Union. The author points to spatial, social, and financial diversification related to the access to cultural events and participation in them using the example of Podlaskie voivodship. He propounds greater professionalisation related to running cultural institutions at the level of the voivodship as well as conducting pro-development cultural policy by local government units. He also stresses the need for activating local and regional communities in connection with cultural events and participation in organizing them. Participation in culture at the local and regional level and prudent government policy in this area are the foundation for building a strong social and regional identity.Przedmiotem analizy w artykule jest udział administracji jednostek samorządu terytorialnego w prowadzeniu instytucji kultury. Na tle ogólnopolskim przeanalizowana została sytuacja w województwie podlaskim. Główne zagadnienia opracowania to zasady i metody finansowania instytucji kultury przez regionalny samorząd terytorialny oraz różne sposoby pozyskiwania przez instytucje kultury dodatkowego,&nbsp; pozabudżetowego dofinansowania ich działalności, szczególnie ze środków pochodzących z budżetu Ministerstwa Kultury i Dziedzictwa Narodowego oraz Unii Europejskiej. Autor wskazuje na zróżnicowanie przestrzenne, społeczne i finansowe związane z dostępem do wydarzeń kulturalnych i uczestnictwem w nich na przykładzie województwa podlaskiego. Postuluje większą profesjonalizację zarządzania instytucjami kultury na szczeblu województwa oraz prowadzenie prorozwojowej polityki kulturalnej przez jednostki samorządu terytorialnego. Akcentuje również konieczność aktywizowania lokalnych i regionalnych społeczności w związku z wydarzeniami kulturalnymi i współuczestniczenia mieszkańców w ich organizowaniu. Uczestnictwo w kulturze na poziomie lokalnym i regionalnym oraz rozważna polityka władz w tym zakresie są podstawą budowania silnych więzi społecznych i tożsamości regionalnej mieszkańców