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Exploiting Feature Selection in Human Activity Recognition: Methodological Insights and Empirical Results Using mobile sensor data

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IEEE ACCESS 2022年 10卷 64043-64058页

作者： Manca, Marco Manolo Pes, Barbara Riboni, Daniele Univ Cagliari Dipartimento Matemat & Informat I-09124 Cagliari Italy

Human Activity Recognition (HAR) using mobile sensor data has gained increasing attention over the last few years, with a fast-growing number of reported applications. The central role of machine learning in this field has been discussed by a vast amount of research works, with several strategies proposed for processing raw data, extracting suitable features, and inducing predictive models capable of recognizing multiple types of daily activities. Since many HAR systems are implemented in resource-constrained mobile devices, the efficiency of the induced models is a crucial aspect to consider. This paper highlights the importance of exploiting dimensionality reduction techniques that can simplify the model and increase efficiency by identifying and retaining only the most informative and predictive features for activity recognition. More in detail, a large experimental study is presented that encompasses different feature selection algorithms as well as multiple HAR benchmarks containing mobile sensor data. Such a comparative evaluation relies on a methodological framework that is meant to assess not only the extent to which each selection method is effective in identifying the most predictive features but also the overall stability of the selection process, i.e., its robustness to changes in the input data. Although often neglected, in fact, the stability of the selected feature sets is important for a wider exploitability of the induced models. Our experimental results give an interesting insight into which selection algorithms may be most suited in the HAR domain, complementing and significantly extending the studies currently available in this field.

关键词： Feature extraction Filtering algorithms Computational efficiency Classification algorithms Predictive models Prediction algorithms Activity recognition Feature selection methods human activity recognition machine learning algorithms mobile sensor data

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CausalCFF: Causal Analysis between User Stress Level and Contextually Filtered Features Extracted from mobile sensor data

CausalCFF: Causal Analysis between User Stress Level and Con...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Zhang, Panyu Jung, Gyuwon Ahmed, Uzair Lee, Uichin Graduate School of Data Science KAIST Daejeon South Korea School of Computing KAIST Daejeon South Korea

ISBN: (纸本)9798400713958

Nowadays, it’s possible to deliver interventions through mobile technologies to improve users’ mental and physical health. Causal analysis may help researchers identify the potential causes of the health issues and design proper interventions. However, in previous studies, causal analysis is mainly conducted between single sensor data features such as walking activity duration and perceived stress. There is a lack of research into causal analysis between more complicated behavior features which could be derived from multiple sensor features and target well-being labels. To address this gap, we propose CausalCFF, a framework that investigates causal relationships between contextually filtered behavioral features (e.g., walking duration at workplace locations) and well-being outcomes (e.g., stress). Our analysis identifies frequent workplace visits during periods of reduced home time as the most salient cause for elevated stress levels, highlighting the framework’s ability to target context-specific behavioral biomarkers for human well-being. The code is also made available¹ © 2025 Copyright held by the owner/author(s).

关键词： Causal Analysis Contextually Filtered Features Human Behavior mobile sensor data Stress

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Mining Probabilistic Representative Gathering Patterns for mobile sensor data

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JOURNAL OF INTERNET TECHNOLOGY 2017年第2期18卷 321-332页

作者： Wang, Shuang Yi, Hai Wu, Lina Zhou, Fucai Xiong, Neal N. Northeastern Univ Software Coll Shenyang Liaoning Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai Peoples R China

With the increasing availability of mobile IoT (Internet of Things) devices, it becomes feasible to collect user's trajectory data to discover valuable knowledge. Unfortunately, data uncertainty in trajectory data is inherent due to measurement errors or time-discretized sampling. Because of the nature of dealing with inexact data, new mining approaches are necessary to effectively account for ambiguities in the data. This paper formalizes the concept of probabilistic gathering pattern, which is to find the moving objects that travel together during a certain time period. In contrast to exact spatio-temporal trajectory, an uncertain trajectory is an ordered sequence of random variables. The distance between two uncertain series is also a random variable. Firstly, a new expected distance similarity function is designed to cluster the moving objects. Secondly, in order to avoid considerable redundancy in the complete set of gathering patterns, a new algorithm to only mining the representative set of probabilistic gathering patterns is presented, and some pruning strategies are proposed to greatly improve the mining efficiency. Finally, the efficiency of the approaches is validated by extensive experiments.

关键词： Spatio-temporal data Probabilistic gathering pattern mobile sensor data The Internet of things

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Mining Probabilistic Representative Gathering Patterns for mobile sensor data

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网际网路技术学刊 2017年第2期18卷 321-332页

作者： Shuang Wang Hai Yi Lina Wu Fucai Zhou Neal N. Xiong

关键词： Spatio-temporal data Probabilistic gathering pattern mobile sensor data The Internet of things

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Efficient Top-k Result Diversification for mobile sensor data 36

Efficient Top-k Result Diversification for Mobile Sensor Dat...

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36th IEEE International Conference on Distributed Computing Systems (ICDCS)

作者： Yokoyama, Masahiro Hara, Takahiro Osaka Univ Grad Sch Informat Sci & Technol Dept Multimedia Engn 1-5 Yamadaoka Suita Osaka 5650871 Japan

ISBN: (纸本)9781509014828

Due to recent developments in sensor technologies, mobile sensor device use has become widespread, and many researchers have been attempting to leverage data collected by these devices. We call such data 'mobile sensor data'. mobile sensor data are geo-referenced data with environmental attribute values;and they enable us to determine the geographical distribution of hot spots by retrieving data (such as higher air-pollution index values) with comparatively extreme environmental attribute values. Top-k search result diversification in geographical space is valid for applications of this sort. However, the preference scores for data items are different from each user's interest, and must be calculated for each query from scratch. In this case, the computational cost of a naive method is excessively high when the amount of mobile sensor data is very large. Thus, in this paper, we propose an efficient top-k search result diversification method for mobile sensor data. In a naive method, it is necessary to scan all data existing in a given query range when seeking the best data. Our proposed method, however, can reduce the amount of scanned data by exploiting cluster information, and the query result can thereby be returned much more rapidly. Moreover, a number of optimization problems can be solved using only one cluster file set. Experimental results show that our proposed method involves short computation time and reduces the disk IO cost in comparison with a naive method.

关键词： mobile sensor data top-k result diversification hot spot detection participatory sensing

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Inferring Social Relationships from mobile sensor data 23

Inferring Social Relationships from Mobile Sensor Data

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23rd International Conference on World Wide Web (WWW)

作者： Hsieh, Hsun-Ping Li, Cheng-Te Natl Taiwan Univ Grad Inst Networking & Multimedia Taipei 106 Taiwan

ISBN: (纸本)9781450327459

While mobile sensors are ubiquitous nowadays, the geographical activities of human beings are feasible to be collected and the geospatial interactions between people can be derived. As we know there is an underlying social network between mobile users, such social relationships are hidden and hold by service providers. Acquiring the social network over mobile users would enable lots of applications, such as friend recommendation and energy-saving mobile DB management. In this paper, we propose to infer the social relationships using the sensor data, which contains the encounter records between individuals, without any knowledge about the real friendships in prior. We propose a two-phase prediction method for the social inference. Experiments conducted on the CRAWDAD data demonstrate the encouraging results with satisfying prediction scores of precision and recall.

关键词： Social network social relationship inference mobile sensor data

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Feature Selection in mobile Activity Recognition: A Comparative Study 22

Feature Selection in Mobile Activity Recognition: A Comparat...

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22nd IEEE International Conference on mobile data Management (IEEE MDM)

作者： Loddo, Andrea Pes, Barbara Riboni, Daniele Univ Cagliari Dept Math & Comp Sci Via Osped 72 I-09124 Cagliari Italy

ISBN: (纸本)9781665428453

mobile sensor-based activity recognition is a growing research field with important applications areas, such as healthcare and well-being. data collected from multiple sensors, including smartphone sensors that are now ubiquitous, can be exploited to build predictive models capable of recognizing actions and activities performed by humans in their daily life. This involves several processing steps, from the cleansing of raw data to the extraction of suitable features and the induction of proper classifiers. Dimensionality reduction techniques may also be important for the efficiency and the exploitability of the induced models, especially when dealing with multi-sensor data leading to high-dimensional feature vectors. In such a scenario, feature selection algorithms can be very useful to identify and retain only the most informative and predictive features. However, little research has so far investigated which selection approaches may be most appropriate in sensor-based activity recognition tasks. To give a contribution in this direction, our paper compares the performance of different feature selection methods, both univariate and multivariate, on a public domain benchmark containing smartphone sensor data. We performed a comprehensive evaluation considering the extent to which each method effectively identifies the most predictive features and the overall stability of the selection process, i.e., its robustness to changes in the input data. Our results give interesting insight on which methods may be most suited in this domain, showing that it is possible to significantly reduce the data dimensionality without compromising the activity recognition performance.

关键词： human activity recognition mobile sensor data machine learning feature selection

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Spatial structures of tourism destinations: A trajectory data mining approach leveraging mobile big data

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ANNALS OF TOURISM RESEARCH 2020年 84卷 102973-102973页

作者： Park, Sangwon Xu, Yang Jiang, Liu Chen, Zhelin Huang, Shuyi Hong Kong Polytech Univ Sch Hotel & Tourism Management Kowloon TST East 61917 Sci Museum Rd Hong Kong Peoples R China Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hung Hom Kowloon ZS6086-FSouth WingBlock Z181 Chatham Rd South Hong Kong Peoples R China

The advancement of mobile technology provides an opportunity to obtain the real-time information of travelers, such as their spatial and temporal behaviors, during their visits to a destination. This study analyzes a large scale mobile phone dataset that captures the cellphone trace of international travelers who visited South Korea. We apply a trajectory data mining approach to understand the spatial structures of tourist activities within three different destinations. Through spatial clustering analysis and sequential pattern mining, the study reveals multiple "hot spots" (or popular areas) in travel destinations and spatial interactions across these places. As a result, this paper provides important tourism implications integrating spatial models with destination planning, which is the foundation of tourism design.

关键词： Smart tourism Destination planning Big data mobile sensor data Trajectory pattern mining

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Integration of air pollution data collected by mobile sensors and ground-based stations to derive a spatiotemporal air pollution profile of a city

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INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2019年第11期33卷 2218-2240页

作者： Leung, Yee Zhou, Yu Lam, Ka-Yu Fung, Tung Cheung, Kwan-Yau Kim, Taehong Jung, Hanmin Chinese Univ Hong Kong Dept Geog & Resource Management Shatin Hong Kong Peoples R China Chinese Univ Hong Kong Inst Future Cities Shatin Hong Kong Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China Korea Inst Oriental Med Convergence Serv Ctr Daejeon South Korea Korea Inst Sci & Technol Informat Future Med Div Daejeon South Korea

Air pollution has become a serious environmental problem causing severe consequences in our ecology, climate, health, and urban development. Effective and efficient monitoring and mitigation of air pollution require a comprehensive understanding of the air pollution process through a reliable database carrying important information about the spatiotemporal variations of air pollutant concentrations at various spatial and temporal scales. Traditional analysis suffers from the severe insufficiency of data collected by only a few stations. In this study, we propose a rigorous framework for the integration of air pollutant concentration data coming from the ground-based stations, which are spatially sparse but temporally dense, and mobile sensors, which are spatially dense but temporally sparse. Based on the integrated database which is relatively dense in space and time, we then estimate air pollutant concentrations for given location and time by applying a two-step local regression model to the data. This study advances the frontier of basic research in air pollution monitoring via the integration of station and mobile sensors and sets up the stage for further research on other spatiotemporal problems involving multi-source and multi-scale information.

关键词： Air pollution ground-based station data integration mobile sensor data spatio-temporal variation

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Impacts on Carbon Dioxide Emissions from the Replacement of Conventional Buses by Electric Buses

Impacts on Carbon Dioxide Emissions from the Replacement of ...

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作者： Feng Mao Zhiheng Li Kai Zhang Dapeng Zhang Tsinghua Shenzhen International Graduate School Tsinghua University Yixing Network Traffic Technology Co. Ltd

Public transport buses traversing in urban areas emit extensive carbon dioxide（CO）. It is critical to understand and estimate the characteristics of carbon emissions for transit buses to achieve a low-carbon transportation system. This paper compares the CO emissions between electric buses and conventional buses. We use the mobile sensor data of electric buses collected from Shenzhen to calculate CO emissions. To evaluate the CO emissions impacts of electric buses, we design four scenarios of different replacement rates of a conventional buses fleet by electric buses as a case study. The results demonstrate that the CO emissions of electric buses fleet can be reduced 34896.512 tons in comparison with conventional fuel buses by 2023. And the reduction rate of CO emissions will be 20.601%. Moreover, the reduction rate of CO emissions will be 0 if the CO emissions intensity is 742 g CO/kwh.

关键词： Electric buses CO2 emissions mobile sensor data CO2 emissions intensity

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