Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Effect of sparsity-aware time–frequency analysis on dynamic hand gesture classification with radar micro-Doppler signatures

Dynamic hand gesture recognition is of great importance in human-computer interaction. In this study, the authors investigate the effect of sparsity-driven time-frequency analysis on hand gesture classification. The time-frequency spectrogram is first obtained by sparsity-driven time-frequency analysis. Then three empirical micro-Doppler features are extracted from the time-frequency spectrogram and a support vector machine is used to classify six kinds of dynamic hand gestures. The experimental results on measured data demonstrate that, compared to traditional time-frequency analysis techniques, sparsity-driven time-frequency analysis provides improved accuracy and robustness in dynamic hand gesture classification

Practical classification of different moving targets using automotive radar and deep neural networks

In this work, the authors present results for classification of different classes of targets (car, single and multiple people, bicycle) using automotive radar data and different neural networks. A fast implementation of radar algorithms for detection, tracking, and micro-Doppler extraction is proposed in conjunction with the automotive radar transceiver TEF810X and microcontroller unit SR32R274 manufactured by NXP Semiconductors. Three different types of neural networks are considered, namely a classic convolutional network, a residual network, and a combination of convolutional and recurrent network, for different classification problems across the four classes of targets recorded. Considerable accuracy (close to 100% in some cases) and low latency of the radar pre-processing prior to classification (∼0.55 s to produce a 0.5 s long spectrogram) are demonstrated in this study, and possible shortcomings and outstanding issues are discussed

Micro-Doppler Based Human-Robot Classification Using Ensemble and Deep Learning Approaches

Radar sensors can be used for analyzing the induced frequency shifts due to micro-motions in both range and velocity dimensions identified as micro-Doppler ($\boldsymbol{\mu}$-D) and micro-Range ($\boldsymbol{\mu}$-R), respectively. Different moving targets will have unique $\boldsymbol{\mu}$-D and $\boldsymbol{\mu}$-R signatures that can be used for target classification. Such classification can be used in numerous fields, such as gait recognition, safety and surveillance. In this paper, a 25 GHz FMCW Single-Input Single-Output (SISO) radar is used in industrial safety for real-time human-robot identification. Due to the real-time constraint, joint Range-Doppler (R-D) maps are directly analyzed for our classification problem. Furthermore, a comparison between the conventional classical learning approaches with handcrafted extracted features, ensemble classifiers and deep learning approaches is presented. For ensemble classifiers, restructured range and velocity profiles are passed directly to ensemble trees, such as gradient boosting and random forest without feature extraction. Finally, a Deep Convolutional Neural Network (DCNN) is used and raw R-D images are directly fed into the constructed network. DCNN shows a superior performance of 99\% accuracy in identifying humans from robots on a single R-D map.Comment: 6 pages, accepted in IEEE Radar Conference 201

Statistical properties of the Disk Counterparts of Type II Spicules from simultaneous observations of RBEs in Ca II 8542 and H{\alpha}

Spicules were recently found to exist as two types when a new class of so-called type II spicules was discovered at the solar limb with Hinode. The type II spicules have been linked with on-disk observations of Rapid Blue-shifted Excursions (RBEs) in the Ha and Ca 8542 lines. Here we analyze observations optimized for the detection of RBEs in both Ha and Ca 8542 simultaneously at a high temporal cadence taken with CRISP at the SST. This study used a high-quality time sequence for RBEs at different blue-shifts and employed an automated detection routine to detect a large number of RBEs in order to expand on the statistics of RBEs. We find that the number of detected RBEs is dependent on the Doppler velocity of the images on which the search is performed. Detection of RBEs at lower velocities increases the estimated number of RBEs to the same order of magnitude expected from limb spicules. This shows that RBEs and type II spicules are exponents of the same phenomenon. We provide evidence that Ca 8542 RBEs are connected to Ha RBEs and are located closer to the network regions with the Ha RBEs being the continuation, and show that RBEs have an average lifetime of 83.9 s when observed in both spectral lines with Doppler velocity ranges of 10-25 km/s in Ca 8542 and 30-50 km/s in Ha. In addition, we determine the transverse motion of a much larger sample of RBEs than previous studies and find that like type II spicules, RBEs undergo significant transverse motions, 5-10 km/s. Finally, we find that the intergranular jets discovered in BBSO are a subset of RBEs.Comment: Accepted for publication in the Astrophysical Journal, 15 pages, 10 figure

The NIRSPEC Ultracool Dwarf Radial Velocity Survey

We report the results of an infrared Doppler survey designed to detect brown dwarf and giant planetary companions to a magnitude-limited sample of ultracool dwarfs. Using the NIRSPEC spectrograph on the Keck II telescope, we obtained approximately 600 radial velocity measurements over a period of six years for a sample of 59 late-M and L dwarfs spanning spectral types M8/L0 to L6. A subsample of 46 of our targets have been observed on three or more epochs. We rely on telluric CH4 absorption features in the Earth's atmosphere as a simultaneous wavelength reference and exploit the rich set of CO absorption features found in the K-band spectra of cool stars and brown dwarfs to measure radial velocities and projected rotational velocities. For a bright, slowly rotating M dwarf standard we demonstrate a radial velocity precision of 50 m/s, and for slowly rotating L dwarfs we achieve a typical radial velocity precision of approximately 200 m/s. This precision is sufficient for the detection of close-in giant planetary companions to mid-L dwarfs as well as more equal mass spectroscopic binary systems with small separations (a<2 AU). We present an orbital solution for the subdwarf binary LSR1610-0040 as well as an improved solution for the M/T binary 2M0320-04. We also combine our radial velocity measurements with distance estimates and proper motions from the literature to estimate the dispersion of the space velocities of the objects in our sample. Using a kinematic age estimate we conclude that our UCDs have an age of 5.0+0.7-0.6 Gyr, similar to that of nearby sun-like stars. We simulate the efficiency with which we detect spectroscopic binaries and find that the rate of tight (a<1 AU) binaries in our sample is 2.5+8.6-1.6%, consistent with recent estimates in the literature of a tight binary fraction of 3-4%. (abridged)Comment: 39 pages, 20 figures. Accepted for publication in Ap

Prognostic impact of coronary microcirculation abnormalities in systemic sclerosis: a prospective study to evaluate the role of non-invasive tests

INTRODUCTION: Microcirculation dysfunction is a typical feature of systemic sclerosis (SSc) and represents the earliest abnormality of primary myocardial involvement. We assessed coronary microcirculation status by combining two functional tests in SSc patients and estimating its impact on disease outcome. METHODS: Forty-one SSc patients, asymptomatic for coronary artery disease, were tested for coronary flow velocity reserve (CFR) by transthoracic-echo-Doppler with adenosine infusion (A-TTE) and for left ventricular wall motion abnormalities (WMA) by dobutamine stress echocardiography (DSE). Myocardial multi-detector computed tomography (MDCT) enabled the presence of epicardial stenosis, which could interfere with the accuracy of the tests, to be excluded. Patient survival rate was assessed over a 6.7- ± 3.5-year follow-up. RESULTS: Nineteen out of 41 (46%) SSc patients had a reduced CFR (≤2.5) and in 16/41 (39%) a WMA was observed during DSE. Furthermore, 13/41 (32%) patients showed pathological CFR and WMA. An inverse correlation between wall motion score index (WMSI) during DSE and CFR value (r = -0.57, P <0.0001) was observed; in addition, CFR was significantly reduced (2.21 ± 0.38) in patients with WMA as compared to those without (2.94 ± 0.60) (P <0.0001). In 12 patients with abnormal DSE, MDCT was used to exclude macrovasculopathy. During a 6.7- ± 3.5-year follow-up seven patients with abnormal coronary functional tests died of disease-related causes, compared to only one patient with normal tests. CONCLUSIONS: A-TTE and DSE tests are useful tools to detect non-invasively pre-clinical microcirculation abnormalities in SSc patients; moreover, abnormal CFR and WMA might be related to a worse disease outcome suggesting a prognostic value of these tests, similar to other myocardial diseases

Trumpeting M Dwarfs with CONCH-SHELL: a Catalog of Nearby Cool Host-Stars for Habitable ExopLanets and Life

We present an all-sky catalog of 2970 nearby ($d \lesssim 50$ pc), bright ($J< 9$) M- or late K-type dwarf stars, 86% of which have been confirmed by spectroscopy. This catalog will be useful for searches for Earth-size and possibly Earth-like planets by future space-based transit missions and ground-based infrared Doppler radial velocity surveys. Stars were selected from the SUPERBLINK proper motion catalog according to absolute magnitudes, spectra, or a combination of reduced proper motions and photometric colors. From our spectra we determined gravity-sensitive indices, and identified and removed 0.2% of these as interloping hotter or evolved stars. Thirteen percent of the stars exhibit H-alpha emission, an indication of stellar magnetic activity and possible youth. The mean metallicity is [Fe/H] = -0.07 with a standard deviation of 0.22 dex, similar to nearby solar-type stars. We determined stellar effective temperatures by least-squares fitting of spectra to model predictions calibrated by fits to stars with established bolometric temperatures, and estimated radii, luminosities, and masses using empirical relations. Six percent of stars with images from integral field spectra are resolved doubles. We inferred the planet population around M dwarfs using $Kepler$ data and applied this to our catalog to predict detections by future exoplanet surveys.Comment: Accepted to MNRAS 22 figures, 3 tables, 2 electronic tables. Electronic tables are available as links on this pag