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自回数据集包含9个活动类的数据  6项步行活动,3项静坐活动

自回数据集包含9个活动类的数据 6项步行活动,3项静坐活动

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Computer Classification

Sadiq Sani, Nirmalie Wiratunga, Kay CooperRobert Gordon UniversityAberdeen, UKData Set Information:The SELFBACK dataset......

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    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    Sadiq Sani, Nirmalie Wiratunga, Kay Cooper
    Robert Gordon University
    Aberdeen, UK


    Data Set Information:

    The SELFBACK dataset contains data of 9 activity classes; 6 ambulatory activities
    and 3 sedentary activities, performed by 33 participants.
    Data are recorded with two tri-axial accelerometers sampling at 100Hz, mounted on
    the dominant side wrist and the thigh of the participant.

    **Application**
    The dataset can be used for human activity recognition by developing algorithms for
    pre-processing, feature extraction, sensor fusion, segmentation and classification.

    ** Data collection method **
    Each participant performed an activity for approximately 3 minutes.
    ** Sensors**
    Axivity AX3 3-Axis Logging Accelerometer
    - sampling frequency -- 100Hz
    - range -- 8g
    ** Activity Classes**
    - Walking Upstairs
    - Walking Downstairs
    - Walking in slow pace
    - Walking in medium pace
    - Walking in fast pace
    - Jogging
    - Standing
    - Sitting
    - Lying
    ** Data folder **

    SELFBACK dataset has three folders, two folders one for each sensor modality
    named "w" for wrist and "t" for thigh and an additional folder where two sensor
    modalities are merged using timestamp named "wt" for wrist and thigh.
    Inside "w" and "t" folders, 9 folders can be found, one for each activity class, and
    inside, there are 33 files, one file for each participant.
    Inside "wt" folder, there are 297(33 X 9) files where the file name indicates the
    person and the activity.


    Attribute Information:

    The 4 columns in the files in t and w folder is organized as follows:
    1 -- timestamp
    2 -- x value
    3 -- y value
    4 -- z value
    Min value = -8
    Max value = +8
    The 6 columns in the files in wt folder is organized as follows:
    1 -- wrist x value
    2 -- wrist y value
    3 -- wrist z value
    4 -- thigh x value
    5 -- thigh y value
    6 -- thigh z value
    Min value = -8
    Max value = +8


    Relevant Papers:

    - Sani, S., Wiratunga, N., Massie, S., & Cooper, K. (2016, December).
    SELFBACK--activity recognition for self-management of low back pain. In
    International Conference on Innovative Techniques and Applications of Artificial
    Intelligence (pp. 281-294). Springer, Cham.
    - Sani, S., Massie, S., Wiratunga, N., & Cooper, K. (2017, August). Learning deep
    and shallow features for human activity recognition. In International Conference
    on Knowledge Science, Engineering and Management (pp. 469-482). Springer,
    Cham.
    - Sani, S., Wiratunga, N., Massie, S., & Cooper, K. (2017, June). kNN sampling for
    personalised human activity recognition. In International conference on case-
    based reasoning (pp. 330-344). Springer, Cham.
    - Sani S, Wiratunga N, Massie S, Cooper K. Personalised human activity
    recognition using matching networks. In International Conference on Case-
    based Reasoning 2018 Jul 9 (pp. 339-353). Springer, Cham.
    - Wijekoon, A., Wiratunga, N., Sani, S., Massie, S., & Cooper, K. (2018, July).
    Improving kNN for Human Activity Recognition with Privileged Learning Using
    Translation Models. In International Conference on Case-based Reasoning (pp.
    448-463). Springer, Cham.

    - Wijekoon, A., Wiratunga, N., Sani, S., & Cooper, K. (2020). A knowledge-light
    approach to personalised and open-ended human activity recognition.
    Knowledge-based Systems, 192, 105651.



    Citation Request:

    Sani, S., Wiratunga, N., Massie, S., & Cooper, K. (2016, December).
    SELFBACK--activity recognition for self-management of low back pain. In?International Conference
    on Innovative Techniques and Applications of Artificial Intelligence?(pp. 281-294). Springer, Cham.

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