Human activity detection using smart wearable sensing devices with feed forward neural networks and PSO
Yükleniyor...
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Hospitals must continually monitor their patients’ actions to lower the chance of accidents,
such as patient falls and slides. Human behavior is difficult to track due to the complexity of human
activities and the unpredictable nature of their conduct. As a result, creating a static link that is used
to influence human behavior is challenging, since it is hard to forecast how individuals will think
or act in response to a certain event. Mobility tracking depends on intelligent monitoring systems
that apply artificial intelligence (AI) applications referred to as “categories”. Because motion sensors,
such as gyroscopes and accelerometers, output unconnected data that lack labels, event detection
is a vital task. The fall feature parameters of tridimensional accelerometers and gyroscope sensors
are presented and used, and the classification technique is based on distinguishing characteristics.
This study focuses on the age-old problem of tracking turbulence in motion to improve detection
precision. We trained the model, considering that detection accuracy is limited by factors such as
the subject’s mass, velocity, and gait style. This is performed by employing an experimental dataset.
When we used the sophisticated technique of particle swarm optimization (PSO) in combination with
a four-stage forward neural network (4SFNN) to forecast four different types of turbulent motion, we
observed that the total prediction accuracy was 98.615% accurate.
Açıklama
Anahtar Kelimeler
4SFNN, PSO, AEC, Prediction, Accuracy, MAE, Falling, MSE, SDL, FOL, FKL
Kaynak
Applied Sciences-Basel
WoS Q Değeri
Q1
Scopus Q Değeri
N/A
Cilt
13
Sayı
6
Künye
Al_Hassani, R. T., Atilla, D. C. (2023). Human activity detection using smart wearable sensing devices with feed forward neural networks and PSO. Applied Sciences, 13(6), 3716.
