ohmage: An open Mobile System for Activity and Experience Sampling

Ramanathan, Nithya; Alquaddoomi, Faisal; Falaki, Hossein; George, Dony; Hsieh, Cheng-Kang; Jenkins, John; Ketcham, Cameron; Longstaff, Brent; Ooms, Jeroen; Selsky, Joshua; Tangmunarunkit, Hongsuda; Estrin, Deborah

doi:10.4108/icst.pervasivehealth.2012.248705

Cited by 55 publications

(35 citation statements)

References 30 publications

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“…The accelerometer Pipeline identifies the current physical activity of the user, namely, resting, walking, and cycling, by running a classification algorithm that analyzes some signal features: maximum, minimum, average, standard deviation, and root mean square over the three accelerometer axes. The audio Pipeline recognizes human voice based on some time-domain and frequencydomain features typically considered in the related literature, namely, L1-norm, L2-norm, L-inf norm, Fast Fourier Transform, power spectral density across five different band ranges, and Mel-frequency cepstral coefficients [10,12,14]. These pipelines are representative of real-world workloads, because similar functionalities have been used by existing works based on continuous mobile sensing.…”

Section: Implementation and Experimental Resultsmentioning

confidence: 99%

“…From this logical architecture, applicable to any mobile platform, we realized our MSF for the Android platform that is the most widely adopted one in mobile sensing, also because it allows sensor access even when the system is in standby, a key feature needed by continuous sensing systems and is not available on other mobile platforms (e.g., Apple iOS) [12,14].…”

Section: Msf Logical Architecturementioning

confidence: 99%

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MSF: An Efficient Mobile Phone Sensing Framework

Cardone

Cirri

Corradi

et al. 2013

International Journal of Distributed Sensor Networks

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Recent evolutions in smartphones, today provided with several sensors, have the strong processing capabilities needed to extract from raw sensed data sensor meaningful high-level views of the physical context around the user. A new promising research area called mobile sensing promotes completely decentralized sensing based on smartphone capabilities only. However, current mobile sensing solutions are not very mature; yet, because they are based on ad hoc software solutions tailored to one specific technical problem (e.g., power management, resource locking, etc.), they are difficult to reuse and integrate in different projects, and they do not focus on the performance efficiency of the monitoring support. To overcome those limitations, this paper proposes Mobile Sensing Framework (MSF), a flexible platform to ease the development of mobile sensing applications through the definition of a common set of facilities that mask all low-level technical details in reading and processing raw sensor data. MSF has been optimized also to enhance performances for Android-based systems, and we report an extensive set of experimental results that assess our architecture and quantitatively compare it with a selection of other mobile sensing systems by showing that MSF outperforms them by presenting lower CPU usage and memory footprints.

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Section: Implementation and Experimental Resultsmentioning

confidence: 99%

Section: Msf Logical Architecturementioning

confidence: 99%

MSF: An Efficient Mobile Phone Sensing Framework

Cardone

Cirri

Corradi

et al. 2013

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…Along with the success of MCS apps such as Waze [8] and SeeClickFix [19], researchers are now interested in developing general-purpose MCS platforms that allow for the publishing and management of tasks with different sensing purposes. Ohmage [20], PRISM [21], and Hive [22] are promising startups towards this direction. These platforms play a major role on task publishing and data collection, but optimized multi-task allocation has not received attention.…”

Section: Related Workmentioning

confidence: 99%

ActiveCrowd: A Framework for Optimized Multitask Allocation in Mobile Crowdsensing Systems

Guo

Liu

et al. 2017

IEEE Trans. Human-Mach. Syst.

209

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Abstract-Worker selection is a key issue in Mobile Crowd Sensing (MCS). While previous worker selection approaches mainly focus on selecting a proper subset of workers for a single MCS task, multi-task-oriented worker selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes ActiveCrowd, a worker selection framework for multi-task MCS environments. We study the problem of multi-task worker selection under two situations: worker selection based on workers' intentional movement for time-sensitive tasks and unintentional movement for delay-tolerant tasks. For time-sensitive tasks, workers are required to move to the task venue intentionally and the goal is to minimize the total distance moved. For delay-tolerant tasks, we select workers whose route is predicted to pass by the task venues and the goal is to minimize the total number of workers. Two Greedy-enhanced Genetic Algorithms are proposed to solve them. Experiments verify that the proposed algorithms outperform baseline methods under different experiment settings (scale of task sets, available workers, varied task distributions, etc.).

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“…Conversation behavior was analyzed in [13] by recording only small fragments of audio (30 s every 12.5 min) and allowing participants to exclude recordings from further analysis, though only a very few used those options [12]. The combination of questionnaires and objective data, especially on mobile phones, was proposed in [17] e.g., but without considering audio. A technically simpler approach was to carry out interviews and recordings of natural acoustics at the same time [16].…”

Section: Introductionmentioning

confidence: 99%