Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
The Potential of Wearables to Automatically Detect Depression

The Potential of Wearables to Automatically Detect Depression

Kreutzmann, Tim

Paperback

Programming

ISBN10: 3346889394
ISBN13: 9783346889393
Publisher: Grin Verlag
Published: Jun 6 2023
Pages: 132
Weight: 0.40
Height: 0.31 Width: 5.83 Depth: 8.27
Language: English
Master's Thesis from the year 2022 in the subject Computer Science - Commercial Information Technology, grade: 1,7, University of Hannover (Institut für Wirtschaftsinformatik), language: English, abstract: Wearable devices are frequently used to continuously collect physiological and behavioral data using integrated sensors. The strong correlation between activity levels and psychiatric disorders implies that these data offer potential in the diagnosis of depression. The objective of this master thesis is to answer the question of how activity data from wearables can be used to diagnose depression. To this end, the following research question is posed: How can wearables be applied to automatically detect depression states? To answer the research question, a secondary data analysis of the Depresjon dataset was conducted. The dataset includes motor activity data from 23 unipolar and bipolar depressed subjects and 32 healthy controls. Statistical features were extracted from the motor activity data to subsequently feed a random forest classifier. Using the motor activity signal from the wearable, the results show a sensitivity value of 0.941, indicating that depressed subjects are correctly classified 94.1% of the time, and a specificity value of 0.936, indicating that healthy control subjects are correctly classified 93.6% of the time.

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