Adaptive Momentum-Based Motion Detection Approach and Its Application on Handoff in Wireless Networks

Abstract

Positioning and tracking technologies can detect the location and the movement of mobile nodes (MNs), such as cellular phone, vehicular and mobile sensor, to predict potential handoffs. However, most motion detection mechanisms require additional hardware (e.g., GPS and directed antenna), costs (e.g., power consumption and monetary cost) and supply systems (e.g., network fingerprint server). This paper proposes a Momentum of Received Signal Strength (MRSS) based motion detection method and its application on handoff. MRSS uses the exponentially weighted moving average filter with multiple moving average window size to analyze the received radio signal. With MRSS, an MN can predict its motion state and make a handoff trigger at the right time without any assistance from positioning systems. Moreover, a novel motion state dependent MRSS scheme called Dynamic MRSS (DMRSS) algorithm is proposed to adjust the motion detection sensitivity. In our simulation, the MRSS- and DMRSS-based handoff algorithms can reduce the number of unnecessary handoffs up to 44% and save battery power up to 75%.

Mobile Applications in Evaluations of Knee Joint Kinematics: A Pilot Study

“…Measurement precision is an essential factor that requires deep consideration when investigating the diagnostic potential of wireless sensors. The accuracy of such testing methods has been confirmed by van Acht et al [21], Chung et al [22], Djurić-Jovičić et al [23], and finally Chardonnens et al [24]. The reliability of online data collection has been proved to match offline-based methods by Ermes et al [25] The utility of mobile application used in wireless motion capture systems for physiotherapeutic purposes has been confirmed in a few studies.…”

Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning

“…Poppe gives an overview of vision based human motion analysis in [ 30 ]. Aside from vision-based solutions, sensor-based approaches are also extensively adopted in biomedical systems [ 31 – 34 ]. Most of the previous motion recognition related research assumed that the Micro-Electro-Mechanical Systems (MEMS) inertial sensors used are fixed on a human body [ 35 – 38 ] (e.g., in a pocket, clipped to a belt or on a lanyard) and that an inference model can be trained according to a handful of body positions.…”