Build Cryptographic System From Multi-Biometrics Using Meerkat Algorithm

Salman, Duha

doi:10.25195/20174521

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using sensors in clothing opens up new possibilities in fitness and health tracking and activity recognition. Typically, machine learning Algorithms are used to interpret sensors measurements to track and monitor various body positions, such as the independent monitoring of humans for applications including assisted living [11], rehabilitation, and monitoring [12], and health monitoring systems [13][14][15], especially during COVID_19 [16], remote epileptic patient supervising system [17], sports applications [18], wheelchair movement control [19], detect information about crime locations [20], monitoring oil [21], and detection of human movements [22].…”

Section: Introductionmentioning

confidence: 99%

Loose Garments Effects on Wearable Sensors in Human Activity Tracking Applications

Fadhil,

Shareef

2023

MMEP

View full text Add to dashboard Cite

With the advent of miniature sensor technology, it is now possible to collect data on various aspects of human movement under free-living conditions. This technology has the potential to be used in activity monitoring systems in several areas, including health, military, sports applications, and human monitoring. The majority of research in wearables technology is focused on skin-mounted sensors or embedded in tight clothes. However, most of our daily clothes are loose or contain wide parts. This paper is interested in analyzing measurements of an accelerometer embedded in loose clothes. Experiments are conducted using a wearable node embedded in an oblong piece of cloth to emulate loose clothes. The piece is attached to a participant's arm while performing daily activities. Measurements are collected and presented in both time and frequency domains. Finally, activity measurements are classified using SVM and KNN algorithms. Results indicate that the differences in measurements between loose and tight clothes are noticeable in both domains, but the degradation in classification accuracy is unneglectable. When the sensor was embedded in 10 cm long piece of cloth the classification accuracies are over 80% and 90% for SVM and KNN, respectively, which is approximate, 5% less than the tight clothes accuracies.

show abstract