RF sensing for continuous monitoring of human activities for home consumer applications

Abstract

Radar for indoor monitoring is an emerging area of research and development, covering and supporting different health and wellbeing applications of smart homes, assisted living, and medical diagnosis. We report on a successful RF sensing system for home monitoring applications. The system recognizes Activities of Daily Living(ADL) and detects unique motion characteristics, using data processing and training algorithms. We also examine the challenges of continuously monitoring various human activities which can be categorized into translation motions (active mode) and in-place motions (resting mode). We use the range-map, offered by a range-Doppler radar, to obtain the transition time between these two categories, characterized by changing and constant range values, respectively. This is achieved using the Radon transform that identifies straight lines of different slopes in the range-map image. Over the in-place motion time intervals, where activities have insignificant or negligible range swath, power threshold of the radar return micro-Doppler signatures,which is employed to define the time-spans of individual activities with insignificant or negligible range swath. Finding both the transition times and the time-spans of the different motions leads to improved classifications, as it avoids decisions rendered over time windows covering mixed activities.

Arm Motion Classification Using Time-Series Analysis of the Spectrogram Frequency Envelopes

“…Finding the onset and offset times of arm motion becomes necessary to determine the individual motion boundaries and time span. These times can be obtained from the PBC [44,45], which measures the signal energy in the spectrogram within specific frequency bands. In particular, we compute…”

Derivative Target Line (DTL) for Continuous Human Activity Detection and Recognition

“…This allows to find the onset and offset of each inter class activities, e.g. walking-falling merged [10]. We introduce the Target Line (TL) detection process for finding the position of the target (human being).…”

“…The algorithm reduces the noise outside the true Target Line (TL) and monitors the position of the subject over time. This method can be used for data sequences of any length and does not require a time limitation as in [10]. The proposed Derivative Target Line (DTL) is then used and combined with the limitation of the possible classes for classification.…”

SimHumalator: An Open Source WiFi Based Passive Radar Human Simulator For Activity Recognition

“…Humans are non-rigid bodies whose motion when illuminated by radio-frequency (RF) fields, gives rise to frequency modulations, popularly known as micro-Dopplers. Over the last decade, radar sensors have used these micro-Doppler signatures to detect, track and classify human activities for numerous applications ranging from law enforcement, security, and surveillance purposes [1]- [5] to various ubiquitous sensing applications such as assisted living for the elderly e.g., fall detection [6]- [10], bio-medical applications for non-intrusively monitoring patients [11]- [14], and smart home applications such as occupancy detection [15], [16] and hand gesture recognition [17], [18]. Micro-Dopplers have been observed with active and passive radar sensors [19]- [23].…”