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MHEALTH(移动健康)数据集

MHEALTH(移动健康)数据集

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Health Classification

Oresti Banos, Department of Computer Architecture and Computer Technology, University of Granada Rafael Garcia, Departme......

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    README.md

    Oresti Banos, Department of Computer Architecture and Computer Technology, University of Granada
    Rafael Garcia, Department of Computer Architecture and Computer Technology, University of Granada
    Alejandro Saez, Department of Computer Architecture and Computer Technology, University of Granada

    Email to whom correspondence should be addressed: oresti '@' ugr.es (oresti.bl '@' gmail.com)


    Data Set Information:

    The MHEALTH (Mobile HEALTH) dataset comprises body motion and vital signs recordings for ten volunteers of diverse profile while performing several physical activities. Sensors placed on the subject's chest, right wrist and left ankle are used to measure the motion experienced by diverse body parts, namely, acceleration, rate of turn and magnetic field orientation. The sensor positioned on the chest also provides 2-lead ECG measurements, which can be potentially used for basic heart monitoring, checking for various arrhythmias or looking at the effects of exercise on the ECG.

    ----------------------------------------------------------------------------------------------------------------------
    DATASET SUMMARY:
    #Activities: 12
    #Sensor devices: 3
    #Subjects: 10
    ----------------------------------------------------------------------------------------------------------------------

    EXPERIMENTAL SETUP
    The collected dataset comprises body motion and vital signs recordings for ten volunteers of diverse profile while performing 12 physical activities (Table 1). Shimmer2 [BUR10] wearable sensors were used for the recordings. The sensors were respectively placed on the subject's chest, right wrist and left ankle and attached by using elastic straps (as shown in the figure in attachment). The use of multiple sensors permits us to measure the motion experienced by diverse body parts, namely, the acceleration, the rate of turn and the magnetic field orientation, thus better capturing the body dynamics. The sensor positioned on the chest also provides 2-lead ECG measurements which are not used for the development of the recognition model but rather collected for future work purposes. This information can be used, for example, for basic heart monitoring, checking for various arrhythmias or looking at the effects of exercise on the ECG. All sensing modalities are recorded at a sampling rate of 50 Hz, which is considered sufficient for capturing human activity. Each session was recorded using a video camera. This dataset is found to generalize to common activities of the daily living, given the diversity of body parts involved in each one (e.g., frontal elevation of arms vs. knees bending), the intensity of the actions (e.g., cycling vs. sitting and relaxing) and their execution speed or dynamicity (e.g., running vs. standing still). The activities were collected in an out-of-lab environment with no constraints on the way these must be executed, with the exception that the subject should try their best when executing them.

    ACTIVITY SET
    The activity set is listed in the following:
    L1: Standing still (1 min)
    L2: Sitting and relaxing (1 min)
    L3: Lying down (1 min)
    L4: Walking (1 min)
    L5: Climbing stairs (1 min)
    L6: Waist bends forward (20x)
    L7: Frontal elevation of arms (20x)
    L8: Knees bending (crouching) (20x)
    L9: Cycling (1 min)
    L10: Jogging (1 min)
    L11: Running (1 min)
    L12: Jump front & back (20x)
    NOTE: In brackets are the number of repetitions (Nx) or the duration of the exercises (min).

    A complete and illustrated description (including table of activities, sensor setup, etc.) of the dataset is provided in the papers presented in the section a€?Citation Requestsa€?.


    Attribute Information:

    The data collected for each subject is stored in a different log file: 'mHealth_subject


    Relevant Papers:

    Banos, O., Garcia, R., Holgado, J. A., Damas, M., Pomares, H., Rojas, I., Saez, A., Villalonga, C. mHealthDroid: a novel framework for agile development of mobile health applications. Proceedings of the 6th International Work-conference on Ambient Assisted Living an Active Ageing (IWAAL 2014), Belfast, Northern Ireland, December 2-5, (2014).

    Nguyen, L. T., Zeng, M., Tague, P., Zhang, J. (2015). Recognizing New Activities with Limited Training Data. In IEEE International Symposium on Wearable Computers (ISWC).



    Citation Request:

    Use of this dataset in publications must be acknowledged by referencing the following publications:

    Banos, O., Garcia, R., Holgado, J. A., Damas, M., Pomares, H., Rojas, I., Saez, A., Villalonga, C. mHealthDroid: a novel framework for agile development of mobile health applications. Proceedings of the 6th International Work-conference on Ambient Assisted Living an Active Ageing (IWAAL 2014), Belfast, Northern Ireland, December 2-5, (2014).

    Banos, O., Villalonga, C., Garcia, R., Saez, A., Damas, M., Holgado, J. A., Lee, S., Pomares, H., Rojas, I. Design, implementation and validation of a novel open framework for agile development of mobile health applications. BioMedical Engineering OnLine, vol. 14, no. S2:S6, pp. 1-20 (2015).

    We recommend to refer to this dataset as the 'MHEALTH dataset' in publications.
    We would appreciate if you send us an email (oresti.bl '@' gmail.com) to inform us of any publication using this dataset.

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