We propose in this paper the use of Wavelet transform (WT) to detect human falls using a ceiling mounted Doppler range control radar. The radar senses any motions from falls as well as nonfalls due to the Doppler effect. The WT is very effective in distinguishing the falls from other activities, making it a promising technique for radar fall detection in nonobtrusive inhome elder care applications. The proposed radar fall detector consists of two stages. The prescreen stage uses the coefficients of wavelet decomposition at a given scale to identify the time locations in which fall activities may have occurred. The classification stage extracts the time-frequency content from the wavelet coefficients at many scales to form a feature vector for fall versus nonfall classification. The selection of different wavelet functions is examined to achieve better performance. Experimental results using the data from the laboratory and real inhome environments validate the promising and robust performance of the proposed detector.

We propose a non-wearable hydraulic bed sensor system that is placed underneath the mattress to estimate the relative systolic blood pressure of a subject, which only differs from the actual blood pressure by a scaling and an offset factor. Two types of features are proposed to obtain the relative blood pressure, one based on the strength and the other on the morphology of the bed sensor BCG pulses. The relative blood pressure is related to the actual by a scale and an offset factor that can be obtained through calibration. The proposed system is able to extract the relative blood pressure more accurately with a less sophisticated sensor system compared to those from the literature. We tested the system using a dataset collected from 48 subjects right after active exercises. Comparison with the ground truth obtained from the blood pressure cuff validates the promising performance of the proposed system, where the mean correlation between the estimate and the ground truth is near 90% for the strength feature and 83% for the morphology feature.

We propose a simple and robust method to detect heartbeats using the ballistocardiogram (BCG) signal that is produced by a hydraulic bed sensor placed under the mattress. The proposed method is found beneficial especially when the BCG signal does not display consistent J-peaks, which can often be the case for overnight, in-home monitoring, especially with frail seniors. Heartbeat detection is based on the short-time energy of the BCG signal. Compared with previous methods that rely on the J-peaks observed from the BCG amplitude, we are able to achieve considerable improvement even when significant distortions are present. Test results are included for different BCG waveform patterns from older adults.

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A lane change warning system is proposed, which gets the surrounding vehicle' motion states based on V2V communication. The safe distance between ego-vehicle and rear vehicle in target lane is analyzed according to the goal of both collision avoidance and vehicle following safety. Safe distance between ego-vehicle and front vehicle in target lane is analyzed to ensure safety in emergency collision avoidance. Safe distances between ego-vehicle and front and rear vehicles in original lane are analyzed to realize safe following. Meanwhile, driving style index is proposed to adapt the warning system to different kinds of drivers. Simulation results show that the lane change warning system can make a composite analysis of the collision risk during the lane-changing, and provide an accurate real-time warning to assist drivers. Index Terms-lane change warning, minimum safe distance, collision warning, V2V communication, driver assist system Manuscript received May 4th, 2014. Dang Ruina is with China North Vehicle Research Institute,

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