Wearable Fall Detector Using Recurrent Neural Networks

Falls have become a relevant public health issue due to their high prevalence and negative effects in elderly people. Wearable fall detector devices allow the implementation of continuous and ubiquitous monitoring systems. The effectiveness for analyzing temporal signals with low energy consumption is one of the most relevant characteristics of these devices. Recurrent neural networks (RNNs) have demonstrated a great accuracy in some problems that require analyzing sequential inputs. However, getting appropriate response times in low power microcontrollers remains a difficult task due to their limited hardware resources. This work shows a feasibility study about using RNN-based deep learning models to detect both falls and falls’ risks in real time using accelerometer signals. The effectiveness of four different architectures was analyzed using the SisFall dataset at different frequencies. The resulting models were integrated into two different embedded systems to analyze the execution times and changes in the model effectiveness. Finally, a study of power consumption was carried out. A sensitivity of 88.2% and a specificity of 96.4% was obtained. The simplest models reached inference times lower than 34 ms, which implies the capability to detect fall events in real-time with high energy efficiency. This suggests that RNN models provide an effective method that can be implemented in low power microcontrollers for the creation of autonomous wearable fall detection systems in real-time

137CS gamma-ray detection at Summit, Greenland

Global fall-out from atmospheric testing of thermonuclear weapons produced horizon markers corresponding to the initiation of testing in 1953 and the maximum fall-out in 1963. The radioactive isotope 137Cs associated with these events has a half-life of 30.2 years. Therefore, with the appropriate radiation detectors, this fall-out can be used as a long-term temporal indicator in glaciers and snowpack. A prototype γ-ray detector system was successfully tested and was used to make in-situ measurements of the 137Cs marker in a borehole at Summit, Greenland. The system consisted of a 7.6 cm by 7.6 cm NaI (Tl) scintillation crystal/photomultiplier detector, commercial pre-amplifier, amplifier and power supplies, and a microcomputer-based pulse-height analyzer. The measurements were made in boreholes of 25.4 cm and 12.7 cm diameter to depths of 22 m. Based on the results reported here, the γ-ray detection technique promises to be a powerful way to locate quickly horizon markers in the field. -Author

Results from the Commissioning of the ATLAS Pixel Detector with Cosmic data

The ATLAS pixel detector is the innermost detector of the ATLAS experiment at the Large Hadron Collider at CERN. With approximately 80 million readout channels, the ATLAS silicon pixel detector is a high-acceptance, high-resolution, low-noise tracking device. Providing the desired refinement in charged track pattern recognition capability in order to meet the stringent track reconstruction requirements, the pixel detector largely defines the ability of ATLAS to effectively resolve primary and secondary vertices and perform efficient flavor tagging essential for discovery of new physics. Being the last sub-system installed in ATLAS by July 2007, the pixel detector was successfully connected, commissioned, and tested in situ while meeting an extremely tight schedule, and was ready to take data upon the projected turn-on of the LHC. Since fall 2008, the pixel detector has been included in the combined ATLAS detector operation, collecting cosmic muon data. Details from the pixel detector installation and commissioning, as well as details on calibration procedures and the results obtained with collected cosmic data, are presented along with a summary of the detector status.Comment: To be published in the proceedings of DPF-2009, Detroit, MI, July 2009, eConf C090726. Contents: 9 pages, 13 figures, 9 reference

A New Scintillator Tile/Fiber Preshower Detector for the CDF Central Calorimeter

A detector designed to measure early particle showers has been installed in front of the central CDF calorimeter at the Tevatron. This new preshower detector is based on scintillator tiles coupled to wavelength-shifting fibers read out by multi-anode photomultipliers and has a total of 3,072 readout channels. The replacement of the old gas detector was required due to an expected increase in instantaneous luminosity of the Tevatron collider in the next few years. Calorimeter coverage, jet energy resolution, and electron and photon identification are among the expected improvements. The final detector design, together with the R&D studies that led to the choice of scintillator and fiber, mechanical assembly, and quality control are presented. The detector was installed in the fall 2004 Tevatron shutdown and started collecting colliding beam data by the end of the same year. First measurements indicate a light yield of 12 photoelectrons/MIP, a more than two-fold increase over the design goals.Comment: 5 pages, 10 figures (changes are minor; this is the final version published in IEEE-Trans.Nucl.Sci.

Beam test results of 3D fine-grained scintillator detector prototype for a T2K ND280 neutrino active target

An upgrade of the long baseline neutrino experiment T2K near detector ND280 is currently being developed with the goal to reduce systematic uncertainties in the prediction of number of events at the far detector Super-Kamiokande. The upgrade program includes the design and construction of a new highly granular fully active scintillator detector with 3D WLS fiber readout as a neutrino target. The detector of about $200\times 180\times 60~cm^3$ in size and a mass of $\sim$2.2~tons will be assembled from about $2\times10^6$ plastic scintillator cubes of $1\times1\times1~cm^3$. Each cube is read out by three orthogonal Kuraray Y11 Wave Length Shifting (WLS) fibers threaded through the detector. A detector prototype made of 125 cubes was assembled and tested in a charged particle test beam at CERN in the fall of 2017. This paper presents the results obtained on the light yield and timing as well as on the optical cross-talk between the cubes.Comment: 5 pages, 8 figure

Construction and Expected Performance of the Hadron Blind Detector for the PHENIX Experiment at RHIC

A new Hadron Blind Detector (HBD) for electron identification in high density hadron environment has been installed in the PHENIX detector at RHIC in the fall of 2006. The HBD will identify low momentum electron-positron pairs to reduce the combinatorial background in the $e^{+}e^{-}$ mass spectrum, mainly in the low-mass region below 1 GeV/c$^{2}$. The HBD is a windowless proximity-focusing Cherenkov detector with a radiator length of 50 cm, a CsI photocathode and three layers of Gas Electron Multipliers (GEM). The HBD uses pure CF$_{4}$ as a radiator and a detector gas. Construction details and the expected performance of the detector are described.Comment: QM2006 proceedings, 4 pages 3 figure

Anti-Fall: A Non-intrusive and Real-time Fall Detector Leveraging CSI from Commodity WiFi Devices

Fall is one of the major health threats and obstacles to independent living for elders, timely and reliable fall detection is crucial for mitigating the effects of falls. In this paper, leveraging the fine-grained Channel State Information (CSI) and multi-antenna setting in commodity WiFi devices, we design and implement a real-time, non-intrusive, and low-cost indoor fall detector, called Anti-Fall. For the first time, the CSI phase difference over two antennas is identified as the salient feature to reliably segment the fall and fall-like activities, both phase and amplitude information of CSI is then exploited to accurately separate the fall from other fall-like activities. Experimental results in two indoor scenarios demonstrate that Anti-Fall consistently outperforms the state-of-the-art approach WiFall, with 10% higher detection rate and 10% less false alarm rate on average.Comment: 13 pages,8 figures,corrected version, ICOST conferenc

Performance of the ATLAS Detector on First Single Beam and Cosmic Ray Data

We report on performance studies of the ATLAS detector obtained with first single LHC (Large Hadron Collider) beam data in September 2008, and large samples of cosmic ray events collected in the fall of 2008. In particular, the performance of the calorimeter, crucial for jet and missing transverse energy measurements, is studied. It is shown that the ATLAS experiment is ready to record the first LHC collisions.Comment: 4 pages, 6 figures, proceedings contribution of the SUSY 2009 conference in Bosto

A neural network based fall detector

In this project we present an intelligent fall detector system based on a 3-axis accelerometer and a neural network model that allows recognizing several possible motion situations and performing an emergency call only when a fall situation occurs, with low false negatives rate and low false positives rate. The system is based on a two module platform. The first one is a Mobile Station (MS) and should be carried always by the person. An accelerometer is implemented in this module and its information is transferred via a radio-frequency channel (RF) to the Base Station (BS). The BS is fixed and is connected to a GSM (Global System for Mobile communication) module. A neural network model was built into the BS and is able to identify falls from other possible motion situations, based on the received information. According to the neural network response the system sends a SMS (Short Message Service) to a destination number requesting for assistance