An RFID Based Localization and Mental Stress Recognition System Using Wearable Sensors

Raed, Mhd.; Yön, Semih; Güneş, Alime; Kotenko, Igor; Doynikova, Elena; Polubaryeva, Anna

doi:10.5220/0011796000003414

Cited by 2 publications

(1 citation statement)

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“…Additionally, another algorithm detects wandering based on defining safe zones and implementing data mining [34]. Another promising method involves using wireless physiological sensors and wearable biosensors, including heart rate and blood pressure sensors, accelerometers, and gyroscopes, in conjunction with trajectory tracking techniques and machine learning algorithms such as deterministic tree-based algorithms to detect the occurrence of emotional arousal in the patient while wandering [35,36]. The use of advanced technologies such as the internet of things (IOT), Long-Short Term Memory (LSTM), neural networks, and the Gray model have also contributed to the accurate detection of wandering in another study of this group [37,38].…”

Section: Localization Combined With the Geofence-based Techniquementioning

confidence: 99%

A Wandering Detection Method Based on Processing GPS Trajectories Using the Wavelet Packet Decomposition Transform for People with Cognitive Impairment

Jafarpournaser,

Delavar,

Noroozian

2023

IJGI

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The increasing prevalence of cognitive disorders among the elderly is a significant consequence of the global aging phenomenon. Wandering stands out as the most prominent and challenging symptom in these patients, with potential irreversible consequences such as loss or even death. Thus, harnessing technological advancements to mitigate caregiving burdens and disease-related repercussions becomes paramount. Numerous studies have developed algorithms and smart healthcare and telemedicine systems for wandering detection. Broadly, these algorithms fall into two categories: those estimating path complexity and those relying on historical trajectory data. However, motion signal processing methods are rarely employed in this context. This paper proposes a motion-signal-processing-based algorithm utilizing the wavelet packet transform (WPT) with a fourth-order Coiflet mother wavelet. The algorithm identifies wandering patterns solely based on patients’ positional data on the current traversed path and variations in wavelet coefficients within the frequency–time spectrum of motion signals. The model’s independence from prior motion behavior data enhances its compatibility with the pronounced instability often seen in these patients. A performance assessment of the proposed algorithm using the Geolife open-source dataset achieved accuracy, precision, specificity, recall, and F-score metrics of 83.06%, 92.62%, 83.06%, 83.06%, and 87.58%, respectively. Timely wandering detection not only prevents irreversible consequences but also serves as a potential indicator of progression to severe Alzheimer’s in patients with mild cognitive impairment, enabling timely interventions for preventing disease progression. This underscores the importance of advancing wandering detection algorithms.

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