Combining Smartphone and Smartwatch Sensor Data in Activity Recognition Approaches: an Experimental Evaluation

Ramos, Felipe; Lorayne, Anne; Costa, Antonio; Sousa, Reudismam Rolim de; Almeida, Hyggo; Perkusich, Ângelo

doi:10.18293/seke2016-040

Cited by 22 publications

(10 citation statements)

References 17 publications

(40 reference statements)

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“…An accuracy of over 90% has been recorded for detecting drunk people. In [109], six different activities with three different algorithms using time-domain features have been proposed. A 90% accuracy has been reported for the J48 algorithm.…”

Section: Journal Of Sensorsmentioning

confidence: 99%

Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

et al. 2019

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Smart device industry allows developers and designers to embed different sensors, processors, and memories in small-size electronic devices. Sensors are added to enhance the usability of these devices and improve the quality of experience through data collection and analysis. However, with the era of big data and machine learning, sensors’ data may be processed by different techniques to infer various hidden information. The extracted information may be beneficial to device users, developers, and designers to enhance the management, operation, and development of these devices. However, the extracted information may be used to compromise the security and the privacy of humans in the era of Internet of Everything (IoE). In this work, we attempt to review the process of inferring meaningful data from smart devices’ sensors, especially, smartphones. In addition, different useful machine learning applications based on smartphones’ sensors data are shown. Moreover, different side channel attacks utilizing the same sensors and the same machine learning algorithms are overviewed.

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Section: Journal Of Sensorsmentioning

confidence: 99%

Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

et al. 2019

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“…In [22] the authors use inertial data from a wrist-mounted device to detect activities performed on household objects. In [21] the authors propose to combine smartwatches and smartphones for activity recognition and evaluate different features. A Deep Belief Network (DBN) composed by stacked Restricted Boltzmann Machines (RBMs) is used in [5] for detecting activities based on spectrograms of acceleration data.…”

Section: Related Workmentioning

confidence: 99%

Semantic Place Understanding for Human–Robot Coexistence—Toward Intelligent Workplaces

Rosa

Patané

et al. 2019

IEEE Trans. Human-Mach. Syst.

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Recent introductions of robots to everyday scenarios have revealed unprecedented opportunities for collaboration and social interaction between robots and people. However, to date, such interactions are hampered by a significant challenge: having a semantic understanding of their environment. Even simple requirements, such as "a robot should always be in the kitchen when a person is there", are difficult to implement without prior training. In this paper, we advocate that robot-people coexistence can be leveraged to enhance the semantic understanding of the shared environment, and improve situation awareness. We propose a probabilistic framework that combines human activity sensor data generated by smart wearables with low level localisation data generated by robots. Based on this low level information and leveraging colocation events between a user and a robot, it can reason about the two types of semantic information: 1) semantic maps, i.e. the utility of each room, and 2) space usage semantics: tracking humans and robots thru rooms of different utilities. The proposed system relies on twoway sharing of information between the robot and the user. In the first phase, user activities indicative of room utility are inferred from wearable devices and shared with the robot, enabling it to gradually build a semantic map of the environment. In the second phase, via colocation events, the robot teaches the user device to recognize the type of room where they are co-located. Over time, robot and user become increasingly independent and capable of semantic scene understanding.

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“…Also, smartwatches and smartphones have been used simultaneously for the automatic recognition of activities. In (Ramos et al 2016) the use of two intelligent devices with MEMS inertial sensors shows an increase the accuracy of physical activity recognition. The results show that wearables are a viable alternative to automatic recognition using commercial devices.…”

Section: Wearables For Motion and Activity Detectionmentioning

confidence: 99%

Wearable technology for occupational risk assessment: Potential avenues for applications

Pavón¹,

Sigcha²,

Arezes³

et al. 2018

Occupational Safety and Hygiene VI

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This paper analyzes the opportunities offered by wearable technology for occupational risk assessment. A wide range of electronic devices known as "wearables" include sensors that provide interesting features with the potential to be used in workers' monitoring, sending alert signs (e.g., visual, sound, haptic) and training. In this paper, it has been identified the primary applications in which wearables are being used for risk assessment (motion and activity detection, recognition of work-related musculoskeletal disorders, fall detection, evaluation of exposure to different physical agents, evaluation of exposure to chemical agents, and location of potential hazards). Also, it has been identified the opportunities for improvement in the future using the potential of emerging technologies.

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Combining Smartphone and Smartwatch Sensor Data in Activity Recognition Approaches: an Experimental Evaluation

Cited by 22 publications

References 17 publications

Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

Semantic Place Understanding for Human–Robot Coexistence—Toward Intelligent Workplaces

Wearable technology for occupational risk assessment: Potential avenues for applications

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