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Inferring Transportation Mode and Human Activity from Mobile Sensing in Daily Life

Jonathan Liono, Zahraa S. Abdallah, A. K. Qin, Flora D. Salim
2018 Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services - MobiQuitous '18  
mobile sensors (e.g. embedded in smartphones) in real-world dynamic environments.  ...  In this paper, we focus on simultaneous inference of transportation modes and human activities in daily life via modelling and inference from multivariate time series data, which are streamed from off-theshelf  ...  In daily commuting routines, smartphone users travel between urban areas via certain transportation modes.  ... 
doi:10.1145/3286978.3287006 dblp:conf/mobiquitous/LionoAQS18 fatcat:6pbottsiv5gypbvk6j5jl23hqi

Survey on Human Activity Recognition using Smartphone

Adeeba KH, Laheeb Ibrahim
2021 ˜Al-œRafidain journal for computer sciences and mathematics  
Comparison charts highlight their most important aspects such as a type of sensor used, activities, sensor placement, HAR-system type (offline, online), computing device, classifier (type of algorithms  ...  In this survey, A number of previous studies were studied and analyzed, where we prepared a comparison of the research works conducted over the period 2010-2020 in human activity recognition using Smartphone  ...  Fig. 1 . 1 Activity Recognition Procedure 1. Data Collection: Sensors are fitted on the body of people performing daily life Fig. 2 . 2 Types of Machine Learning Algorithms. ‫  ... 
doi:10.33899/csmj.2021.168253 fatcat:odwqb4rar5e3bhtuxeaq4hfzpm

Sensor Fusion for Recognition of Activities of Daily Living

Jiaxuan Wu, Yunfei Feng, Peng Sun
2018 Sensors  
Activity of daily living (ADL) is a significant predictor of the independence and functional capabilities of an individual.  ...  We proposed the ADL Recognition System that utilizes the sensor data from a single point of contact, such as smartphones, and conducts time-series sensor fusion processing.  ...  Chang from the department of Computer Science of Iowa State University for conducting and interpreting the specific concept of the smart home, and grateful for the anonymous reviewers who have made constructive  ... 
doi:10.3390/s18114029 pmid:30463199 fatcat:4pdf4kdqv5fbpjqczkoppopnsq

Continuous Functional Activity Monitoring Based on Wearable Tri-axial Accelerometer and Gyroscope

Yuting Zhang, Inbal Sapir, Stacey Markovic, Robert Wagenaar, Thomas Little
2011 Proceedings of the 5th International ICST Conference on Pervasive Computing Technologies for Healthcare  
A continuous activity recognition algorithm is developed using a decision tree based on time series data and spectrum analysis; this algorithm can identify activities of daily life in three general categories  ...  We propose a novel and practical solution using three small wearable wireless Functional Activity Monitor (FAM) sensors and a smartphone to store, transmit, analyze and update data.  ...  activity recognition algorithm can accurately detect postures (sitting, standing, lying), locomotion (walking) and transitions in a window of one second, and provide a summary of durations of daily activities  ... 
doi:10.4108/icst.pervasivehealth.2011.245966 dblp:conf/ph/ZhangMSWL11 fatcat:ilfxffigevavbndgvbo67iikqq

Human Activity Recognition using Smartwatch and Smartphone: A Review on Methods, Applications, and Challenges

Rana Abdulrahman Lateef, Ayad Rodhan Abbas
2022 Iraqi Journal of Science  
Embedded sensors in smartwatch and smartphone enabled applications to use sensors in activity recognition with challenges for example, support of elderly's daily life .  ...  Most articles published on human activity recognition used a multi -sensors based methods where a number of sensors were tied on different positions on a human body which are not suitable for many users  ...  can perform more than one activity at the same time, therefore, exploring a concurrent activity is also challenge.  ... 
doi:10.24996/ijs.2022.63.1.34 fatcat:dznj4ouzirea5l34mngmptmhle

A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data

Fatima Amjad, Muhammad Hassan Khan, Muhammad Adeel Nisar, Muhammad Shahid Farid, Marcin Grzegorzek
2021 Sensors  
Therefore, this paper presents a two-level hierarchical method to recognize human activities using a set of wearable sensors.  ...  sensory data for recognition because the multiple sequences of the same activity data may have large diversity.  ...  In Reference [21] , a hierarchical model is proposed to recognize the human activities using accelerometer sensor data of smartphone.  ... 
doi:10.3390/s21072368 pmid:33805368 fatcat:5eyi3l3csrhrhmuseeaihmkrje

Activity recognition with smartphone sensors

Xing Su, Hanghang Tong, Ping Ji
2014 Tsinghua Science and Technology  
A hierarchical approach to real-time activity recognition in body sensor networks. Pervasive and Mobile Computing, 2012 [7] N. Ravi, N. Dandekar, P. Mysore, and M. L. Littman.  ...  training and testing Multi subjects, subjects used for testing are different from subjects for training 18 Data Collection  Sensors  Single type of sensor  Single accelerometer  Multiple  ...  Activity Complexity  Multi tasks at the same time Only  ... 
doi:10.1109/tst.2014.6838194 fatcat:yk3rhrspmbg6leesksbydiot2a

REAL-Time Smartphone Activity Classification Using Inertial Sensors—Recognition of Scrolling, Typing, and Watching Videos While Sitting or Walking

Sijie Zhuo, Lucas Sherlock, Gillian Dobbie, Yun Sing Koh, Giovanni Russello, Danielle Lottridge
2020 Sensors  
We then trained a machine learning (ML) model to perform real-time activity recognition of those eight states. We investigated various algorithms and parameters for the best accuracy.  ...  It is currently not possible to access real-time smartphone activities directly, due to standard smartphone privileges and if internal movement sensors can detect them, there may be implications for access  ...  Such recognition could provide benefits such as personalization based on smartphone activities, but also indicates risk for user monitoring via IMU sensors.  ... 
doi:10.3390/s20030655 pmid:31991636 pmcid:PMC7038357 fatcat:ifgfsaevqfh6jl2fr5nr7o2nva

A Mobile Application for Easy Design and Testing of Algorithms to Monitor Physical Activity in the Workplace

Susanna Spinsante, Alberto Angelici, Jens Lundström, Macarena Espinilla, Ian Cleland, Christopher Nugent
2016 Mobile Information Systems  
In fact, HAR via a smartphone or wearable sensor can provide important information regarding the level of daily physical activity, especially in situations where a sedentary behavior usually occurs, like  ...  This paper addresses approaches to Human Activity Recognition (HAR) with the aim of monitoring the physical activity of people in the workplace, by means of a smartphone application exploiting the available  ...  Acknowledgments This work was supported by the COST Action IC1303 AAPELE, Architectures, Algorithms and Platforms for Enhanced Living Environments.  ... 
doi:10.1155/2016/5126816 fatcat:in6bcrgnxzcupmkoa3arjiwnyu

Agatha: Predicting Daily Activities from Place Visit History for Activity-Aware Mobile Services in Smart Cities

Byoungjip Kim, Seungwoo Kang, Jin-Young Ha, Junehwa Song
2015 International Journal of Distributed Sensor Networks  
considering a relatively large number of daily activities defined in the ATUS dataset, that is, 17 activities.  ...  Our evaluation shows that Agatha can predict users' potential activities with up to 90% accuracy for the top 3 activities, more than 80% for the top 2 activities, and about 65% for the top 1 activity while  ...  Instead, we attempt to predict daily routine activities that span a relatively longer term, for example, shopping in a mall, by using unobtrusive smartphone sensors such as Wi-Fi or GPS.  ... 
doi:10.1155/2015/867602 fatcat:kwi7zlwz45gftka4brnsqk52ra

SmokeSense: Online Activity Recognition Framework on Smartwatches [chapter]

Muhammad Shoaib, Ozlem Durmaz Incel, Hans Scholten, Paul Havinga
2018 Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering  
Therefore, in this paper, we present SmokeSense, an online activity recognition (AR) framework developed for both smartphones and smartwatches on Android platform.  ...  In most cases, human activity recognition (AR) with smartphones and smartwatches has been done offline due to the limited resources of these devices.  ...  the smartphone sensors for better activity recognition.  ... 
doi:10.1007/978-3-319-90740-6_7 fatcat:owfliissl5esph46zit4jwqgki

Recognizing human activities from smartphone sensors using hierarchical continuous hidden Markov models

Charissa Ann Ronao, Sung-Bae Cho
2017 International Journal of Distributed Sensor Networks  
Human activity recognition has been gaining more and more attention from researchers in recent years, particularly with the use of widespread and commercially available devices such as smartphones.  ...  To address this, we propose a two-stage continuous hidden Markov model framework, which also takes advantage of the innate hierarchical structure of basic activities.  ...  human activity recognition (HAR). 4, 5 In addition to this, smartphone sensor technologies are developing at an incredible pace.  ... 
doi:10.1177/1550147716683687 fatcat:scfzje4inza27gdvc2jxe3w45u

Application of Machine Learning-Based Pattern Recognition in IoT Devices: Review [chapter]

Zachary Menter, Wei Zhong Tee, Rushit Dave
2021 Proceedings of International Joint Conference on Computational Intelligence  
A multitude of studies has been conducted with the intention of improving speed and accuracy, decreasing complexity, and reducing the overall required processing power of pattern recognition algorithms  ...  After reviewing the applications of different machine learning algorithms, results vary from case to case, but a general conclusion can be drawn that the optimal machine learning-based pattern recognition  ...  optimal algorithm for wearable sensors for human activity recognition using a predefined activity recognition dataset.  ... 
doi:10.1007/978-981-16-3246-4_52 fatcat:ipb2x4ayhberbdctmfsgp475le

Personalized travel mode detection with smartphone sensors

Xing Su, Yuan Yao, Qing He, Jie Lu, Hanghang Tong
2017 2017 IEEE International Conference on Big Data (Big Data)  
In this thesis, we propose new algorithms for travel mode identification using smartphone sensors. The prototype system is built upon the latest Android and iOS platforms with multimodality sensors.  ...  While the state of the art in travel mode recognition mainly relies on large-scale infrastructure-based fixed sensors or on individuals' GPS devices, the emergence of the smartphone provides a promising  ...  For example, learning the pattern of daily smartphone locations via cellular tower connection records in a city could help estimate the transportation volume and predict the traffic of the city.  ... 
doi:10.1109/bigdata.2017.8258065 dblp:conf/bigdataconf/SuYHLT17 fatcat:lfiv7ufbgba7vgq36tetije43q

The Emerging Wearable Solutions in mHealth [chapter]

Fang Zhao, Meng Li, Joe Z. Tsien
2016 Mobile Health Technologies - Theories and Applications  
The marriage of wearable sensors and smartphones have fashioned a foundation for mobile health technologies that enable healthcare to be unimpeded by geographical boundaries.  ...  In particular, the smartphone-based systems, without any external wearables, are summarized and discussed.  ...  [3] , and implement activity recognition [4] .  ... 
doi:10.5772/63557 fatcat:74i6abxp7rarfg6wj7qjgaozmi
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