Recognition of human activity using Internet of Things in a non-controlled environment

Amroun, Hamdi; Ouarti, Nizar; Ammi, Mehdi

doi:10.1109/icarcv.2016.7838750

Cited by 15 publications

(7 citation statements)

References 13 publications

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“…Complete list of surveyed papers, with scenario code and application classification Paper Application Scenario Paper Application Scenario [18] Smart agriculture 0000000 [19] Smart home 1032102A [20] Smart home 0000103 [21] Smart home 1101133 [22] Smart home 0011010 [23] Smart home 1132020 [24] Smart building 0020000 [25] Smart building 1132110 [26] Smart agriculture 0020000A [27] Smart home 1232120 [28] Water management 0021000 [29] Disaster management 1301210 [30] Smart home 0021000A [31] Smart vehicle 1332300 [32] Energy monitoring 0030000 [33] Playful furniture 2012010 [34] Energy monitoring 0031000 [35] Smart agriculture 2023200 [36] Smart home 0101003 [37] Smart city 2030103 [38] Playful furniture 0101123 [39] Smart city 2032013 [40] Smart healthcare 0130030 [41] Smart healthcare 2033020 [42] Smart healthcare 0131113 [43] Robot movement 2130033 [44] Learning device 0230023 [45] Smart healthcare 2133030 [46] Smart healthcare 0231020 [47] Smart building 3001200 [48] Smart healthcare 0312020 [49] Smart home 3022103 [50] Smart healthcare 0321210 [51] Smart city 3032100 [52] Smart healthcare 0322310 [53] Robot movement 3032200 [54] Smart healthcare 0331020 [55] Smart security 3032203 [56] Smart home 1002013...…”

Section: Table 13mentioning

confidence: 99%

An IoT sensor and scenario survey for data researchers

2019

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This work surveys Internet of Things (IoT) experimental research published since 2015. We summarize the IoT state of the art during the last 2 years and extract important data that we apply to enhance the analysis of IoT solutions. The IoT scenario presents a promising universe to data analysis. This raises a number of questions: which are its most popular applications? What is the definition of scale in an IoT application? What sensors are more often used and for what IoT applications? How can a researcher compare IoT scenarios? To investigate these concerns, this survey analyzes 2 years' worth of contributions made in three main scientific publishers. We focus on IoT experiments that were actually implemented using real equipment within one or more scenarios. Our first contribution is the classification of those IoT scenarios into seven main aspects. Each analyzed research study presents a specific configuration of a scenario's variables and sensors. Our second main contribution takes place after the scenario mapping phase. We identify as many as 19 common categories of data types in use. The interrelation among the scenarios and the data types from sensors should assist data researchers in understanding current IoT dynamics.

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Section: Table 13mentioning

confidence: 99%

An IoT sensor and scenario survey for data researchers

2019

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“…Thus, HAR techniques have been applied in many different fields such as healthcare [1], smart environments, and robotics [2][3][4]. As an example, by using IoT technologies, it is possible for a robot assistant to understand the human's behavior and use this inferred information to enable appropriate user authentication tasks [5,6]. Moreover, HAR can be useful to support a remote monitoring between doctors and elderly patients [7].…”

Section: Introductionmentioning

confidence: 99%

Classification of Transition Human Activities in IoT Environments via Memory-Based Neural Networks

2020

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Human activity recognition is a crucial task in several modern applications based on the Internet of Things (IoT) paradigm, from the design of intelligent video surveillance systems to the development of elderly robot assistants. Recently, machine learning algorithms have been strongly investigated to improve the recognition task of human activities. Though, in spite of these research activities, there are not so many studies focusing on the efficient recognition of complex human activities, namely transitional activities, and there is no research aimed at evaluating the effects of noise in data used to train algorithms. In this paper, we bridge this gap by introducing an innovative activity recognition system based on a neural classifier endowed with memory, able to optimize the performance of the classification of both transitional and non-transitional human activities. The system recognizes human activities from unobtrusive IoT devices (such as the accelerometer and gyroscope) integrated in commonly used smartphones. The main peculiarity provided by the proposed system is related to the exploitation of a neural network extended with short-term memory information about the previous activities’ features. The experimental study proves the reliability of the proposed system in terms of accuracy with respect to state-of-the-art classifiers and the robustness of the proposed framework with respect to noise in data.

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“…A number of platforms have been designed, implemented, and tested successfully in order to track subjects’ PA based on the wearable MEMS accelerometer [ 10 , 11 ]. Recent growth of IoT [ 12 , 13 ] and the capability of smart-phones in the past few years made PA recognition a dynamic field of study [ 14 , 15 , 16 ]. Bender et al conducted an empirical study of various fitness devices such as Fitbit Flex, Fitbit Charge HR, Garmin Vívoactive, and Apple Watch to compare PA recognition accuracy and device performance [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Activity Monitoring with a Wrist-Worn, Accelerometer-Based Device

et al. 2018

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This study condenses huge amount of raw data measured from a MEMS accelerometer-based, wrist-worn device on different levels of physical activities (PAs) for subjects wearing the device 24 h a day continuously. In this study, we have employed the device to build up assessment models for quantifying activities, to develop an algorithm for sleep duration detection and to assess the regularity of activity of daily living (ADL) quantitatively. A new parameter, the activity index (AI), has been proposed to represent the quantity of activities and can be used to categorize different PAs into 5 levels, namely, rest/sleep, sedentary, light, moderate, and vigorous activity states. Another new parameter, the regularity index (RI), was calculated to represent the degree of regularity for ADL. The methods proposed in this study have been used to monitor a subject’s daily PA status and to access sleep quality, along with the quantitative assessment of the regularity of activity of daily living (ADL) with the 24-h continuously recorded data over several months to develop activity-based evaluation models for different medical-care applications. This work provides simple models for activity monitoring based on the accelerometer-based, wrist-worn device without trying to identify the details of types of activity and that are suitable for further applications combined with cloud computing services.

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Recognition of human activity using Internet of Things in a non-controlled environment

Cited by 15 publications

References 13 publications

An IoT sensor and scenario survey for data researchers

An IoT sensor and scenario survey for data researchers

Classification of Transition Human Activities in IoT Environments via Memory-Based Neural Networks

Activity Monitoring with a Wrist-Worn, Accelerometer-Based Device

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