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The emerging energy harvesting technology facilitates the development of ubiquitous and everlasting battery-free motion detectors. This paper introduces a robust design of the transient-motion-powered motion detector, which is called ViPSN-pluck. “ViPSN” is the acronym for vibration-powered sensing node while “pluck” stands for the plucking-motion energy harvester. By using a piezo-magneto-elastic structure, ViPSN-pluck can efficiently harvest energy from a transient motion. By properly making good use of this tiny harvested energy, ViPSN-pluck can effectively carry out motion detection and Bluetooth low-energy (BLE) wireless communication. Given the concurrency of mechanical potential energy pre-charging and motion detection, the transient-motion plucking energy harvester used in ViPSN-pluck has the merit of high energy reliability. This unique feature is unprecedented in the solar and RF energy harvesting cases, which might suffer from energy outages under fluctuating irradiance or RF signal strength, respectively. The working principle of ViPSN-pluck, in particular, the dynamic characteristics of the plucking energy harvester and the energy matching between generation and utilization, are discussed in detail to demonstrate the robustness in operation. The cyber-electro-mechanical synergy among the mechanical dynamics, power conditioning circuit, and low-power embedded system is highlighted. The design methodology of ViPSN-pluck provides a valuable reference for the developments of future motion-powered IoT devices.

Radar sensors and micro-Doppler signatures have been widely used to recognize human motions. Apart from the motion classification task, human motion detection has attracted much attention as an emerging topic. A majority of existing motion detectors are designed for a specific motion, such as falling. In some scenarios, however, a broader range of human actions is of interest, hence a general motion detector is desired. In this paper, we propose a radar-based motion detection model named dual generative adversarial network (DGN). The proposed model tackles the detection task as a one-class classification problem and is applicable to detecting various motions. Unlike prior fall detection algorithms, which depend on manually collected alien data, the DGN employs a dual generation scheme to automatically produce valid alien samples in both the pixel level and the semantic level. The model is verified on two measured radar datasets containing individual motions and interactive motions, respectively. The experimental results show that our method outperforms other existing models on the human motion detection task.

Abnormal event detection is an important task in research and industrial applications, which has received considerable attention in recent years. Existing methods usually rely on standard frame-based cameras to record the data and process them with computer vision technologies. In contrast, this paper presents a novel neuromorphic vision based abnormal event detection system. Compared to the frame-based camera, neuromorphic vision sensors, such as Dynamic Vision Sensor (DVS), do not acquire full images at a fixed frame rate but rather have independent pixels that output intensity changes (called events ) asynchronously at the time they occur. Thus, it avoids the design of the encryption scheme. Since events are triggered by moving edges on the scene, DVS is a natural motion detector for the abnormal objects and automatically filters out any temporally-redundant information. Based on this unique output, we first propose a highly efficient method based on the event density to select activated event cuboids and locate the foreground. We design a novel event -based multiscale spatio-temporal descriptor to extract features from the activated event cuboids for the abnormal event detection. Additionally, we build the NeuroAED dataset, the first public dataset dedicated to abnormal event detection with neuromorphic vision sensor. The NeuroAED dataset consists of four sub-datasets: Walking, Campus, Square, and Stair dataset. Experiments are conducted based on these datasets and demonstrate the high efficiency and accuracy of our method.

The article considered the problem of determining the position in three-dimensional space. This is one of the key stages in the creation of UAVs, which over the past few years have become very popular and at the same time useful. Since UAVs are capable of performing a wide range of tasks: monitoring various environmental parameters, delivering food and shopping, extinguishing fires, races and competitions, filming videos, etc. Solving the problem of determining the position in space allows you to obtain the value of the tilt angle using the accelerometer and the instantaneous angular velocity thanks to the gyroscope. That, in turn, allows you to determine the movement, falling of an object or collision with an obstacle in order to go around it. In the work, the configurations of the UAV and communication links were determined. And also the principle of data processing by a microcontroller from combined sensors was given on the example of using the GY-521 module. The main element of the GY-521 module is the MPU-6050 microcircuit, which combines a 3-axis gyroscope, a 3-axis accelerometer and a thermometer in one package. The scope of the module is wide enough, namely for the coordination of various devices - from a simple motion detector to an orientation system for various robots or control of movements by any device. Moreover, a significant advantage of the GY-521 module is its low cost and low power consumption. Thanks to the implementation of internal communication between the STM32 microcontroller and the GY-521 module via the I2C data bus, the operation of the sensor for the orientation of the UAV was tested. Also, a code fragment was shown that demonstrates the module setting that is, setting the operating mode, sensor sensitivity and measuring range of the MPU-6050 sensor and a code fragment for reading the sensor readings. The development environment Keil μVision was used in the work, which is a set of utilities for performing a full range of activities for writing software for microcontrollers in the C++ programming language. The obtained test results of the GY-521 module demonstrate the readings of the accelerometer along the x-axis.

Integrating local motion signals detected by the primary motion detector is crucial for representing a rigid, two-dimensional motion. The nature of motion integration has been studied using stimuli consisting of two superimposed sinusoidal gratings of different orientations, called plaid motion, and it has been shown that humans perceive integrated motion in the direction where the component constraint lines are intersected. We previously found that pigeons and humans perceive different movement directions from plaid motion; pigeons responded to the vector average direction of the gratings. Although this suggests that the underlying processes of motion integration differ between the two species, the viewing distance in the pigeon experiment, which used a touch panel procedure, was much smaller than in typical human experiments. The current study investigated the potential effect of viewing distance on the perception of plaid motion in pigeons. We trained six pigeons to detect whether motion directions were tilted leftward or rightward while a visual display was presented 0 or 40 cm from an operant chamber. The pigeons responded to plaid stimuli for both viewing distance conditions as if they perceived motion in the vector average direction of two-component gratings. The result indicates that the species difference in plaid perception is not an artefact of viewing distance and suggests that pigeons use a different strategy than humans for integrating visual motion.

This paper deals with the description of the development and design of a sensor with wireless data transmission for detecting the movement of people. As part of the development of the sensor, the actual evaluation was performed at the microcontroller level, and a complete solution was executed for deployment in the field of the Internet of Things using IQRF wireless technology. The paper presents methods of digital data processing as well as the design of an IQRF communication module for the actual sensor module. Also included herein is a description of the SW application for visualization of data from the sensor for detecting the movement of persons.

Every object is always moving, and movement of an object can occur at any time. The value of the motion which produced from an object can be very small to very large, and the impact of the movement can be at risk until very risky. For this reason, the movement of objects at risk must be observed whenever changes and observations for data retrieval can be done remotely. The purpose of this research to design an Internet of Things (IoT) devices that can observe and detect changes in the motion of an object. Â The device is designed to be small, around 44 x 48 millimeters with very low power consumption. The design phase begins with recording motion data using the MPU6050 accelerometer sensor as a motion detector, arduino nano as a control device, WiFi ESP8266 as a communication medium for sending data from a receiver apllication motion data with UDP protocol. The test results show that this device is very sensitive to detect changes in the motion and angle of X, Y and Z of an object.

Zebrafish pretectal neurons exhibit specificities for large-field optic flow patterns associated with rotatory or translatory body motion. We investigate the hypothesis that these specificities reflect the input statistics of natural optic flow. Realistic motion sequences were generated using computer graphics simulating self-motion in an underwater scene. Local retinal motion was estimated with a motion detector and encoded in four populations of directionally tuned retinal ganglion cells, represented as two signed input variables. This activity was then used as input into one of three learning networks: a sparse coding network (competitive learning), PCA whitening with subsequent sparse coding, and a backpropagation network (supervised learning). All simulations developed specificities for optic flow which are comparable to those found in a neurophysiological study (Kubo et al. in Neuron 81(6):1344–1359, 2016. https://doi.org/10.1016/j.neuron.2014.02.043), but relative frequencies of the various neuronal responses were best modeled by the sparse coding approach without whitening. We conclude that the optic flow neurons in the zebrafish pretectum do reflect the optic flow statistics. The predicted vectorial receptive fields show not only typical optic flow fields but also “Gabor” and dipole-shaped patterns that likely reflect difference fields needed for reconstruction by linear superposition.

The development of technology towards IoT or the Internet of Things has been very advanced so that the fulfillment of human needs is increasingly easy and comfortable. One example is the need to control and monitor the air temperature in the server room. The server room requires a cool and stable temperature so that the computer devices inside can function properly. In addition, the room also requires a security system to prevent undesirable things, such as intruders or people who are other than server admin officers. These needs can be fulfilled by creating an IoT-based device. Arduino as the main device and online Cayenne web-based application as a medium for controlling Arduino. This device is equipped with a motion detection security system using the PIR Motion Detector HCSR501 sensor while for temperature monitoring using a DHT22 sensor. Control of tools can be done using a web browser on a PC (Personal Computer) and an android smartphone via an internet connection

Pyroelectric infrared (PIR) sensor is widely used as motion detector, due to its sensitivity to the infrared radiation changes (IRC) induced by human motion. In particular, it is also possible to determine the human activity patterns based on the IRC signal by improving the feature-specific sensitivity of the PIR sensors. In this paper, the feature-specific sensitivity is enhanced by visibility modulation at the geometric sensing layer. At the cooperative sensing layer, a novel stereo configuration is used in developing the infrared sensing system, which aims to handle the difficulties caused by occlusion and limited coverage area. The experimental results demonstrate the effectiveness of the developed system in daily activity recognition.

We developed and tested the architecture of a bio-inspired Spiking Neural Network for motion estimation. The computation performed by the retina is emulated by the neuromorphic event-based image sensor DAVIS346 which constitutes the input of our network. We obtained neurons highly tuned to spatial frequency and orientation of the stimulus through a combination of feed-forward excitatory connections modeled as an elongated Gaussian kernel and recurrent inhibitory connections from two clusters of neurons within the same cortical layers. Sums over adjacent nodes weighted by time-variable synapses are used to attain Gabor-like spatio-temporal V1 receptive fields with selectivity to the stimulus’ motion. In order to gain the invariance to the stimulus phase, the two polarities of the events provided by the neuromorphic sensor were exploited, which allowed us to build two pairs of quadrature filters from which we obtain Motion Energy detectors as described in [2]. Finally, a decoding stage allows us to compute optic flow from the Motion Detector layers. We tested the approach proposed with both synthetic and natural stimuli.

The development of Internet of Things (IoT) has placed a strong demand on human motion detection technologies. Motion detectors acts an essential role in many civil applications, such as home security, occupancy and activity monitoring, retail analysis, etc. Emerging energy harvesting technologies provide promising solutions towards the ubiquitous and everlasting deployments of motion detectors. However, most existing battery-free solutions require special environmental conditions and suffer from frequent energy outages. Therefore, their applications are still very limited. This live demonstration exhibits a transient-motion-powered motion detector, named ViPSN-E, where "ViPSN" stands for vibration-powered sensor node [1]; "E" stands for an energy-mode device. ViPSN-E can carry out maintenance-free motion detection and wireless communication by making good use of the energy harvested from an instantaneous motion. The prototype of ViPSN-E is shown in Fig. 1. The system is developed based on ViPSN [1], an open-source development platform specified for vibration- powered IoT devices. ViPSN-E is composed of four parts: a piezo-magneto-elastic structure as energy harvester, a self- powered synchronized switch harvesting on inductor (SP- SSHI) interface circuit [2], an enhanced energy management unit, and a Bluetooth low energy (BLE) unit. The piezo- magneto-elastic harvester is composed of a low-cost piezoelectric cantilever and a pair of magnets, which are used to induce a plucking excitation in a transient and one-way movement.

Aiming at continuous unobtrusive respiration monitoring, motion robustness is paramount. However, some types of motion can completely hide the respiration information and the detection of these events is required to avoid incorrect rate estimations. Therefore, this work proposes a motion detector optimized to specifically detect severe motion of infants combined with a respiration rate detection strategy based on automatic pixels selection, which proved to be robust to motion of the infants involving head and limbs. A dataset including both thermal and RGB (Red Green Blue) videos was used amounting to a total of 43 h acquired on 17 infants. The method was successfully applied to both RGB and thermal videos and compared to the chest impedance signal. The Mean Absolute Error (MAE) in segments where some motion is present was 1.16 and 1.97 breaths/min higher than the MAE in the ideal moments where the infants were still for testing and validation set, respectively. Overall, the average MAE on the testing and validation set are 3.31 breaths/min and 5.36 breaths/min, using 64.00% and 69.65% of the included video segments (segments containing events such as interventions were excluded based on a manual annotation), respectively. Moreover, we highlight challenges that need to be overcome for continuous camera-based respiration monitoring. The method can be applied to different camera modalities, does not require skin visibility, and is robust to some motion of the infants.

This paper presents the design of an Ultra-Low-Power Smart Wearable Multisensory Stereo Camera system, which features the capabilities to be AI -enabled. In this system, two 5MP Omni vision cameras are connected to the same DCMI interface of a dual-core cortex ARM STM32 micro controller. The M7 processor can be configured to capture images from both cameras in a time-synchronized manner, run a Deep neural network, and perform onboard image recognition and object detection. The M4 processor can be interfaced with other sensors such as a motion detector, 6D IMU with a machine learning core, a BLE 5.0 module to communicate with the user, etc. The onboard 6D IMU contains a machine learning core to host a decision tree classifier and relieve some processing load on the M4 processor. The design constraints, the component selection, and interfacing details are discussed in this paper with the complete schematic and layout files so that this stereo camera can be reproduced by the researchers of the common domain and contribute to the development of an open-source blind navigation system.

This paper suggests a motion detection software system that enables us to see the movement around an object or a visual area. Motion detection software is widely preferred as one of the most important security features in recent days. It is used to enhance existing security devices such as motion sensor lighting on indoor and outdoor security cameras. Security cameras are one of the most common devices that use motion detection technology [1]. Now, for better protection, motion detector cameras are preferred. The paper suggested here looks at one of the python motion detection software and detects the movement of objects in front of the camera and determines the time they stay close to the camera.

In Malaysia, the El-Nino phenomenon happen in 2016 was caused the used of home appliances increasing including air-conditioner, fan and air cooler which are used frequently compared to other appliances. This report presents prototype development System for Security and Temperature Control using Microcontroller Based with Smartphone Application. The system has beneficial to reduce human labour besides energy saving and designed for special purposes which can easily maintain the temperature in sorrounding home. The system used several sensors for input parts including LM35 for temperature sensor and Passive Infra-Red (PIR) sensor for motion detector. For security system, the Radio-frequency Identification (RFID) has been used as security input user identification. A microcontroller arduino uno type is used as the system brain in the process part. In outputs part, servo motor has been used as door application while Light-Emitting Diode (LED) and buzzer as indicators when RFID is in use. A smartphone application is implemented in the system which allows the user to control a device remotely including home appliances through Bluetooth module. All data will display in Liquid Crystal Display (LCD) as user reference.

Sleep apnea is defined as a disorder in which the patient feels uneasiness during sleep occurring as a consequence of irregularity in breathing. If sleep apnea is not treated or diagnosed on time, it may lead to life-threatening diseases. The current medical practices to detect sleep apnea rely on contact-based approach. Therefore, this paper is an attempt to propose a non-contact approach for the detection of the sleep apnea disorder in which the breathing rate of the patient is detected using a motion detector followed by the processing of the acquired signal and then the analysis of the signal. The non-contact approach if implemented could be a boon for the medical experts and will open new horizons for the real-time monitoring of parameters of the patients.

The research paper detail with the very important issue that how to provide Security to home which is under threat from the thief for burglary sometime person life will be in danger and now a day, the thief have become more advance in doing burglary not only home but bank shopping mall and gold & diamond jewellery and so to protect not only this building but also our houses, this research paper discuss about protection of building by using PIR sensor wich is a motion detector along with nodeMCU and mobile phone application which is called as Blynk we have to installed this application in our mobile phones this application will be active in phone and a screen will when a motion is detected by pir sensor

Face recognition and biometric analytics is a novel domain of research and enormous algorithms going in this dimension. The identification of smile is a two-stage operation. You sense a face first and then wait for a smile and a motion detector breaks the shot in thousands of zones, Criteria for analysis such as white balance and facial level. If a human smile, by specifying various parameters, the camera detects a facial defect, including closing your eyes, clear teeth, bending your lips and raising your lips. In order to improve the sensitivity of the smile automatic function you should change the camera parameters. Authentic smile identification is more successful when participants, particularly their eyes, (with bangs, etc.) do not cover their faces. Hats, masks or sunglasses may also obstruct the work. You should have a big and open-mouthed grin on your subjects. When your teeth are clear, your camera can still detect smiling better. The presented work is focusing on the face smile analytics with the Perceptual User Interfaces for cumulative performance in the biometric analytics.

Objective Behavioral assessments that effectively predict sleep-wake states were tried in animal research. This study aimed to examine the prediction power of an infrared locomotion detector on the sleep-wake states in ICR (Institute Cancer Research) mice. We also explored the influence of the durations and ways of data processing on the prediction power. Methods The locomotor activities of seven male mice in home cages were recorded by infrared detectors. Their sleep-wake states were assessed by video analysis. Using the receiver operating characteristic curve analysis, the cut-off score was determined, then the area under the curve (AUC) values of the infrared motion detector that predicted sleep-wake states were calculated. In order to improve the prediction power, the four ways of data processing on the prediction power were performed by Matlab 2013b. Results In the initial analysis of raw data, the AUC value was 0.785, but it gradually reached to 0.942 after data summation. The simple data averaging and summation among four different methods showed the maximal AUC value. The 10-minute data summation improved sensitivity (0.889) and specificity (0.901) significantly from the baseline value (sensitivity 0.615; specificity 0.936) (p < 0.001). Conclusion This study suggests that the locomotor activity measured by an infrared motion detector might be useful to predict the sleep-wake states in ICR mice. It also revealed that only simple data summation may improve the predictive power. Using daily locomotor activities measured by an infrared motion detector is expected to facilitate animal research related to sleep-wake states.

By involving only subtle motions of body parts, video-based microaction recognition is a very important but challenging problem. Most existing action recognition methods are developed for general actions, and the current state-of-the-art methods usually largely rely on high-layer features learned from convolutional neural networks (CNNs). High-layer CNN features usually contain more semantic information but less detailed information. However, detailed information can be important for microactions due to the motion subtleness of such actions. In this paper, we propose to more effectively learn midlayer CNN features for enhancing microaction recognition. More specifically, we develop a new dual-branch network for microaction recognition: one branch uses the high-layer CNN features for classification, and the second branch further explores the midlayer CNN features for classification. In the second branch, we introduce a novel subtle motion detector consisting of three modules: 1) a discriminative spatial-temporal feature learning module, which further learns the subtle motion features corresponding to the discriminative spatial-temporal regions, 2) a parallel multiplier attention module, which further refines the features learned in channels and spatial-temporal domains, and 3) an activation fusion module, which fuses the max and average activations from midlayer CNN features for classification. In the experiments, we build a new microaction video dataset, where the micromotions of interest are mixed with other larger general motions such as walking. Comprehensive experimental results verify that the proposed method yields new state-of-the-art performance in two microaction video datasets, while its performance on two general-action video datasets is also very promising.

Object detection and motion parameters estimation are crucial tasks for self-driving vehicle safe navigation in a complex urban environment. In this work we propose a novel real-time approach of temporal context aggregation for motion detection and motion parameters estimation based on 3D point cloud sequence. We introduce an ego-motion compensation layer to achieve real-time inference with performance comparable to a naive odometric transform of the original point cloud sequence. Not only is the proposed architecture capable of estimating the motion of common road participants like vehicles or pedestrians but also generalizes to other object categories which are not present in training data. We also conduct an in-deep analysis of different temporal context aggregation strategies such as recurrent cells and 3D convolutions. Finally, we provide comparison results of our state-of-the-art model with existing solutions on KITTI Scene Flow dataset., Comment: Accepted to ICRA 2020

LuxMeter is widely used to measure light intensity that is required in many medical and agriculture applications. LuxMeter prototypes in the literature have been developed using popular Arduino microcontroller with support from abundant library and programming resources. In this paper, a new prototype of luxmeter system has been designed using ARM Cortex M4 processor based TM4C123 microcontroller, which consumes less power and provides more I/O features compared to Arduino microcontrollers. The details of the hardware and software system design, including I/O interfacing, have been provided to support a wide range of embedded system designs using the TM4C123 microcontroller. The proposed system uses a photoresistor sensor to convert light intensity into a proportional electrical signal and an LCD module to display the final measurement of light intensity via UART interfacing. The microcontroller is programmed in C language to utilize its internal 12-bit analog-to-digital convertor (ADC) module at a sampling rate of 125 KSamples per second. The 12-bit ADC resolution (0–4095) has been normalized to adapt the analog electrical signal level, and the digital output is calibrated as the percent of maximum light illuminance. The system’s functionality in measuring light intensity has been successfully tested and further validated in an experiment of motion detection application.

The article presents the development of an intelligent control system for LED lighting, applicable to autonomous electric lighting installations, outdoor lighting fixtures on posts to illuminate highways, roads, streets and surrounding areas. The system combines all local lighting systems in which the motion sensor and the LED luminaire are integrated into one network. Turning on the LED luminaire at reduced power is carried out automatically when the level of external natural light is less than a certain threshold value. In the case of appearance of an object moving along the motion sensors of neighboring local lighting systems, the speed and direction of movement of the object are determined. In accordance to the speed and direction of the object movement the number of local lighting systems is determined whose LED lamps should be switched on at a higher power and the appearance of the object at the next design point is predicted. The increase in the power of LED lamps is carried out smoothly when the object is approaching the corresponding local lighting system. Due to the dynamic control of the power of the LED luminaires, as moving objects appear in the coverage area of the intelligent lighting system, significant energy savings are achieved. Traffic safety conditions are increased, as the number of LED lamps operating with increased power is determined by the speed of the object, and its possible braking distance will be significantly less than the illuminated section of the roadway. Smooth changes in the power of LED lamps reduce the pressure on the driver of the vehicle. The choice of the motion sensor based on the autodyne radio blocking, which detects moving objects in a given sector of the controlled space, regardless of the time of day and weather conditions, is grounded.

Nowadays the percentage of old aged people that have Alzheimer's disease has increased over the years. Alzheimer's disease is one of the most crucial diseases that our loved ones might acquire. In this study, a device for patients with Alzheimer's disease and a mobile application for the guardian/s to lessen their stress and worries when taking care of the patient were developed. It is called Patient Security & Supervision Equipment(PS & SE), which has a 3-layer security, mainly: [1] The door alert that is equipped with modules like UHF RFID reader which is used as an identifier for the patient, and an activator for the PIR sensor, a motion detector and, a Wi-Fi module for the connection of the door alert to send notifications to the mobile application; [2] The wearable device that is equipped with modules, such as a GPS tracker to know the location of the patient, a GSM module used by the GPS module in sending coordinates to the Firebase database, and a UHF RFID tag. [3] Lastly, the mobile application, named as PSSE, is used by the guardian/s to check the location of the patient. Passcode security is required before accessing the application. Based on a series of testing, the device's accuracy and efficiency for locating the patient depend on the signal/speed of the internet on a certain location. Hence, the system is said to be viable and feasible in the medial industry for its application of Alzheimer's patients and the other mentally challenged patients after the evaluation and validation of a specialist doctor.

This study describes a novel combination of wearable devices and digital music notation system to support hands-free page turning during music performances. Our proposed system follows the pianist’s hands and traces the movements of both wrists in three-axes and sends data to the central computer via a WIFI connection. We used the MIDI format as a standard digital notation system in our study which contains more than 128 notes in 10 octaves. Each piece of music includes a series of smaller and equal sections called measures. Using the MIDI numbering format, the median value of all notes in each measure is available. By comparing the normalized median values and data from the wearables with cross-correlation and dynamic time warping techniques, we can sync these two series, predict the current playing measure, and turn the page at the correct time. The motivation and structure of this study are for pianists; however, this project has the potential to customized for other instruments.

In this research work, an advanced automatic multifunctional compact security system technology is developed using wireless networking system. The security system provides smart security and also alerts the user to avoid the critical circumstances in the daily security issues is held. This system provides a smart solution to the variety of different problems via remote control by the software name Cayenne. This software provides the user to control the system using smart mobile or computer from all over the world and needs to be connected via internet. The system provides general security for essential purposes as the Motion detecting system alerts for any kind of movement inside the area where it is installed, the gas detecting system alerts the user for any type of gas leakage inside the room and also clearing the leaking gas by exhaust fan automatically, the fire detection system detects instantly when a slight fire is emerged also warning the user with alarm, the LDR system is for electrical door lock and it can be controlled by Cayenne using mobile or computer and lastly a home light system which can be turned on/off by the user of Cayenne. Raspberry Pi has been used to connect and control all the necessary equipment. The system provides the most essential security for home and also for corporate world and it is very simple, easy to operate, and consumes small space.

Introduction Modern automated external defibrillators (AEDs) are designed to prevent shock delivery when excessive motion produces rhythm disturbances mimicking ventricular fibrillation (VF). This has been reported as a safety issue in airline operations, where turbulent motion is commonplace. We aimed to evaluate whether all seven AEDs can deliver shock appropriately in a flight simulator under turbulent conditions. Methods The study was performed in a Boeing 747-400 full motion flight simulator in Hong Kong. An advanced life support manikin and arrhythmia generator were used to produce sinus rhythm (SR), asystole, and five amplitudes of VF, with a programmed change to SR in the event of an effective shock being delivered. All rhythms were tested at rest (no turbulence) and at four levels of motion (ground taxi vibration, and mild, moderate and severe in-flight turbulence). Success was defined as: 1. effective shock being delivered where the rhythm was VF successfully converted to SR; 2. no inappropriate shock being delivered for asystole or SR. Results Five AEDs produced acceptable results at all levels of turbulence. Another was satisfactory for VF except at very fine amplitudes. One model was deemed unsatisfactory for in-flight use as its motion detector inhibited shocks at all levels of turbulence. Conclusion Some AEDs designed primarily for ground use may not perform well under turbulent in-flight conditions. AEDs for possible in-flight or other non-terrestrial use should be fully evaluated by manufacturers or end-users before introduction to service.

A visitor's interest in the exhibits at a museum can be understood by analyzing his or her behavior. Conventionally, behavior analysis is performed manually, but it is troublesome to do this in practice. Therefore, our aim is to reduce this trouble by the automation and semi-automation of behavior analysis. We focus on motion detection by machine learning as the first step in this process. To detect motion using machine learning, it is necessary to collect training motions in advance and create a detector. In this study, human models and motions were generated using three-dimensional computer graphics. Then, we synthesized them and collected training motions using a virtual environment. We used the collected training motions to create a detector and detected motions collected from people in a real environment. The results of the experiments indicate that this training data collection method is effective.

This work proposes the development of a sociometric sensor capable of detecting proximity, movement and verbal interaction between people. The sensor will allow measuring the level of existing interrelations between the collaborators of an organization, in order to determine and to measure the organizational climate. The way to evaluate the current organizational climate is subjective, through surveys that are not accurate and that make the results are not adequate. The sensors developed until now focus on proximity or movement, but not on the 3 proposed variables: movement, proximity and speech. The proposed portable device consists of a proximity detector using RSSI (Received Signal Strength Indicator) based on the NodeMCU WIFI ESP 8266 module. It also has a motion detector that uses an accelerometer and finally a voice detection algorithm, acquired via microphone, aimed at determining verbal interaction between people. The validation of the equipment was done by measuring the 3 variables in a sample of 50 people. The results show a percentage of proximity detection of 90% up to a distance of 3 meters between individuals, a percentage of success of 92.5% in the detection of posture and physical activity and a percentage of success of 87.5% in the detection of verbal interaction between people.

Smart home security becomes necessary due to an increasing level of house thievery or home security breach. Previous supporting security system such as CCTV is not enough to support home security. Such system can only focus on monitoring a limited area where it is installed and it is high cost for personal use. This paper presents an Internet of Thing (IoT) system to support home security via remote and timely manner monitoring based on motion and movement detection. The IoT system uses a wireless point to multipoint technique within a node sensor (motion detector) that connects to a microcontroller. Passive Infrared (PIR) and Accelerometer GY-61 are used as the sensor to perform the detection. A web-based dashboard is then deployed to display the monitoring result in timely manner. It receives data from two (2) node sensors that is transmitted via Adafruit server. A Do-It-Yourself (DIY) sensor is then developed. Simulation shows that the sensor system works very well as it is able to detect any object within certain range and any movement of the door and window. The system is one of the practical smart home security solutions that is able to perform timely manner home monitoring based on motion and movement detection.

Summary The detection of visual motion is a fundamental function of the visual system. How motion speed and direction are computed together at the cellular level, however, remains largely unknown. Here, we suggest a circuit mechanism by which excitatory inputs to direction-selective ganglion cells in the mouse retina become sensitive to the motion speed and direction of image motion. Electrophysiological, imaging, and connectomic analyses provide evidence that the dendrites of ON direction-selective cells receive spatially offset and asymmetrically filtered glutamatergic inputs along motion-preference axis from asymmetrically wired bipolar and amacrine cell types with distinct release dynamics. A computational model shows that, with this spatiotemporal structure, the input amplitude becomes sensitive to speed and direction by a preferred direction enhancement mechanism. Our results highlight the role of an excitatory mechanism in retinal motion computation by which feature selectivity emerges from non-selective inputs., Graphical Abstract, Highlights • Space-time wiring between ON DS cells and bipolar cell types was identified • Presynaptic inhibition and NaV channels set the glutamate release dynamics • Preferred direction enhancement supports the tuning to direction and slow speed • Global dendritic summation is involved in computing image motion, Matsumoto et al. find the space-time wiring between the dendrites of ON direction-selective cells and bipolar cell types in the mouse retina. Temporally diverse glutamatergic inputs, which are asymmetrically organized across the dendrites, are globally summated by a preferred direction enhancement mechanism for computing motion speed and direction.

This letter presents a low-power motion gesture recognition system-on-chip (SoC) for smart devices. The SoC incorporates a low-power image sensor and a memory-efficient outermost-edge-based gesture sensing DSP. The DSP utilizes a self-adaptive motion detector that automatically updates a motion-pixel threshold for accurately sensing hand movements. A convolution-based noise-tolerant feature extraction (FE) technique is also developed for preventing detection errors caused by random noises in the images from the low-power sensor. The FE architecture is highly accelerated utilizing parallelisms and pipelining for achieving low-latency real-time gesture recognition. Measurements from a test chip fabricated in 65-nm CMOS show that the SoC consumes $213.7~{\mu }\text{W}$ with only 3- ${\mu }\text{W}$ dynamic power at 30 f/s. The SoC occupies only 0.54 mm2, making it well applicable for wearable devices and sensor nodes. The image sensor is fully operational down to 0.6 V while the DSP can be scaled down to 0.46 V. The average recognition accuracy of the system is 85% while the latency is 1.056 ms.

The classification model which consist of motion detector, object tracker, convolutional sparse coded feature extractor and stacked information-extreme classifier is developed. Proposed model is characterized by low computational complexity and it can be used as labeling dataset gathering tool for deep moveable object detector. Furthermore, training method for moving object detector is developed. The method consisting in unsupervised pre-training feature extractor based on sparse coding neural gas, supervised pre-training and following fine-tuning of stacked information-extreme classifier. Labeling new emerging data through self-labeling for high prediction score cases and manual labeling for low prediction score cases, and following labeled object tracking are also offered. In this case, class balancing using undersampling within dichotomous strategy “one-against-all”. Simulation results on open datasets confirm the suitability of proposed model and training method for practical usage.

This paper presents a water intake monitoring system for animal agriculture that tracks individual animal watering behavior, water quality, and water consumption. The system is deployed in an outdoor environment to reach remote areas. The proposed system integrates motion detectors, cameras, water level sensors, flow meters, Radio-Frequency Identification (RFID) systems, and water temperature sensors. The data collection and control are performed using Arduino microcontrollers with custom-designed circuit boards. The data associated with each drinking event are water consumption, water temperature, drinking duration, animal identification, and pictures. The data and pictures are automatically stored on Secure Digital (SD) cards. The prototypes are deployed in a remote grazing site located in Tucumcari, New Mexico, USA. The system can be used to perform water consumption and watering behavior studies of both domestic animals and wild animals. The current system automatically records the drinking behavior of 29 cows in a two-week duration in the remote ranch.

A compact and low-cost motion detector based on a light-sensitive protein bacteriorhodopsin is fabricated. We show that a single photocell, which is masked with a pattern, can detect the speed and direction of a moving light without complex software algorithms. When light scans the patterned sensing area, the edges of each pattern produce polarity-reversed photocurrents depending on the light bar's speed and direction. Owing to the asymmetry of the pattern, we can detect two or four directions of the moving light. We intend to exploit this unique property in gestural manipulation with a laser pointer or fingers for remote control systems.

It is important to track people's face efficiently and accurately in many Intelligent Transportation Systems ITSs and Safety Driving Assistant Systems SDASs. This paper presents a high-performance and low-cost real-time face tracking system, which runs on general onboard computer with very low CPU consumption. The proposed face tracking system is composed of four modules: the motion detector, face detector, face tracker, and face validator. The motion detector extracts motion areas by using a spatial-temporal bi-differential method with a very low computational cost. The face detector integrates motions into a cascade face detection framework to reject most of non-face scanning-windows to ensure efficient face localization. The face tracker fuses motion feature with color feature to alleviate the drifting problem during tracking. The face validator builds face appearance models online and identifies each specific tracked face to avoid confusion. Experimental results on three challenging video sequences show that the proposed face tracking system outperforms the state-of-the-art face tracker and consumes only 5-13% CPU resources of a low-spec onboard computer while processing in real time. Copyright © 2016 John Wiley & Sons, Ltd.

In this study, we developed 44 working motion detector templates and a behavioral recognition system with the motion templates to recognize worker behaviors, such as turning, squatting, and moving arms, performed by a worker along furrows in the cultivated field of peas. The 44 motion detector templates were designed to identify working motions such as sitting down, moving arms, turning counterclockwise, turning clockwise, up-and-down, side-to-side. The behavioral recognition system with the motion templates was designed to recognize the 12 behavior types and the three behavior category types of turning, squatting, and moving arms behaviors. In the experiments, the developed motion detector templates could consistently well respond to the working motions in the 12 types of behaviors performed by two workers. The behavioral recognition system with the motion templates could recognize the three behavior category types could be robustly determined with the total recognition rate of 80.5%. The results of the experiments demonstrated the applicability and robustness of the developed working motion templates and the behavioral recognition system with the motion templates to recognize the three behavior category types.

Building a reliable and efficient collision avoidance system for unmanned aerial vehicles (UAVs) is still a challenging problem. This research takes inspiration from locusts, which can fly in dense swarms for hundreds of miles without collision. In the locust’s brain, a visual pathway of LGMD-DCMD (lobula giant movement detector and descending contra-lateral motion detector) has been identified as collision perception system guiding fast collision avoidance for locusts, which is ideal for designing artificial vision systems. However, there is very few works investigating its potential in real-world UAV applications. In this paper, we present an LGMD based competitive collision avoidance method for UAV indoor navigation. Compared to previous works, we divided the UAV’s field of view into four subfields each handled by an LGMD neuron. Therefore, four individual competitive LGMDs (C-LGMD) compete for guiding the directional collision avoidance of UAV. With more degrees of freedom compared to ground robots and vehicles, the UAV can escape from collision along four cardinal directions (e.g. the object approaching from the left-side triggers a rightward shifting of the UAV). Our proposed method has been validated by both simulations and real-time quadcopter arena experiments.

The use of technology in teaching and learning is very common today. In physics learning, practicum has an important role, namely deepening the understanding of concepts and applying concepts. However, it is interesting to know the importance of using technology in practicum. This study aims to know to know the student’s Physics education students’ perception on the use of motion detector in linear motion practicum The present study was descriptive research using a qualitative approach. This research was carried out in mechanics experiment class with 16 students as research subjects. This study used students’ reflection and interview in collecting perception’s of the students. The research found that practicum with motion detector has significant role in results obtained. On the other hand, practicum with motion detector has unsignificant role in the student’s carefullness.

This research aims to design, test, and analyze the object tracking application system using a motion detector method that is able to take the output image of several cameras by combining the output images of the two cameras into a wider field of view (FOV). This research uses 2 cameras, i3-6006U laptop camera and Logitech C270 HD webcam using OpenCV and python programming language. The stitching results from the initialization of the two cameras are able to detect the motion of objects performed on panoramatic videos in realtime by showing time in detail starting from days, dates, hours, minutes and seconds. This object tracking application system has been tested in Adi Sucipto airport exactly in the area of arrival and departure of passengers. The system can detected the motion in the stitching images by exploring it in red rectanguler in the real time video.

Security Systems plays a very important role in today's modernized industrialized era. Throughout our life, the hard earned assets and valuables things are expected to be safeguarded under certain security features which meet the inquest of the requisite. It is basically designed in order to avoid the risk of vulnerabilities to our valuable items. In this technological world, the system includes biometrics along with digital code lock which response in the way for matching or mismatching the code. Any mismatch to the series of authentication during verification is done raises an alert sound. For biometrical analysis Iris scanner and vein detector is being used which will be monitored with the help of microcontroller through the sensors of the biometric sensors. A keypad will be used for the registered codes such as unique passwords and registered number followed by a wireless motion detector. Any movement occurs to the output of wireless motion detector will be easily sensed by the microcontroller resulting an alert sound. For best assurance, this process of secured authenticity will be active $24\times 7$ that includes at night time as well.

Wireless power transfer (WPT) is a possible way to achieve cheap, rapid and large-scale deployment of battery-less IoT smart sensors. However, the key challenge in designing WPT-supplied smart sensors is to ensure that the harvested power is higher than the consumed power in presence of strong WPT path loss. This paper presents the design and optimization of a smart sensor supplied by 2.45-GHz RF power and performing infrared-based motion detection and Bluetooth Low Energy (BLE) communication. The full system (RF energy harvester, power management, sensor transducer and interface, control, data processing and wireless transmission) is implemented using low-power off-the-shelf components. In the sensing sub-system, low average power is achieved using efficient duty-cycling of the analog front-end. In the harvesting sub-system, architecture choice and design of the matching network and rectifier circuit allow to optimize the total power harvesting efficiency (PHE). The implemented smart sensor can operate reliably with an incident RF power as low as −10.9 dBm. The sensing sub-system exhibits an average power of 4.3 μW and a maximum number of transmitted event packets of 60 packets per hour.

Remote activity monitoring can support aging-in-place for the elderly, providing crucial capabilities such as fall detection. Falls are the leading cause of accidental death in people aged 65 and over in the United States. The chances of survival are high with low impact on quality of life when prompt assistance is provided after a fall. Radar is at the forefront of research on non-wearable technologies for fall detection and monitoring of activities of daily living for eldercare. Various features extracted from Doppler motion signatures have been proposed in the literature for radar-based fall detection. However, none of these features were specifically designed to provide the most discrimination between the fall and non-fall motion classes. In this paper, we perform linear discriminant analysis (LDA) of Doppler signatures as a first step towards identification of the most discriminative features. LDA performance is evaluated using real data measurements of various indoor human activities and compared with that of existing radar-based fall detection schemes.

Rapid increase in human population and their encroachment into forest areas have posed several threats to human life and their livelihood. This has subsequently led to a demand for surveillance system that detect the motion of objects in particular areas where threat is possible. This application also detects the pattern of the object i.e. the time at which he comes and goes to their place. For detecting, the presence of particular wildlife in a particular area and their sleep pattern and various things about their identity can be known by the use of this application. The application detects the motion of the object that come in the visual region of the camera and plot the time for which the object remain in its visual sight.