activity
20172020
most citedPattern Recognition Techniques for the Identification of Activities of Daily Living using Mobile Device Accelerometer

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.NI2020

Identifying Packet Loss and Reordering Packets in Keyed UDP Transmissions

Fábio Machado Gil, Nuno M. Garcia, Bárbara Matos +3

The User Datagram Protocol (UDP) and other similar protocols send the application data from the source machine to the destination machine inside segments, without foreseeing nor al…

cs.LG2019

An Efficient Machine Learning-based Elderly Fall Detection Algorithm

Faisal Hussain, Muhammad Basit Umair, Muhammad Ehatisham-ul-Haq +4

Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects o…

cs.CY2017

Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living

Ivan Miguel Pires, Nuno M. Garcia, Nuno Pombo +2

Several types of sensors have been available in off-the-shelf mobile devices, including motion, magnetic, vision, acoustic, and location sensors. This paper focuses on the fusion o…

cs.SD2017

User Environment Detection with Acoustic Sensors Embedded on Mobile Devices for the Recognition of Activities of Daily Living

Ivan Miguel Pires, Nuno M. Garcia, Nuno Pombo +1

The detection of the environment where user is located, is of extreme use for the identification of Activities of Daily Living (ADL). ADL can be identified by use of the sensors av…

cs.CY20171 cited

Pattern Recognition Techniques for the Identification of Activities of Daily Living using Mobile Device Accelerometer

Ivan Miguel Pires, Nuno M. Garcia, Nuno Pombo +2

This paper focuses on the recognition of Activities of Daily Living (ADL) applying pattern recognition techniques to the data acquired by the accelerometer available in the mobile…