DeepWalking: Enabling Smartphone-based Walking Speed Estimation Using Deep Learning

Walking speed estimation is an essential component of mobile apps in various fields such as fitness, transportation, navigation, and health-care. Most existing solutions are focused on specialized medical applications that utilize body-worn motion sensors. These approaches do not serve effectively the general use case of numerous apps where the user holding a smartphone tries to find his or her walking speed solely based on smartphone sensors. However, existing smartphone-based approaches fail to provide acceptable precision for walking speed estimation. This leads to a question: is it possible to achieve comparable speed estimation accuracy using a smartphone over wearable sensor based obtrusive solutions? We find the answer from advanced neural networks. In this paper, we present DeepWalking, the first deep learning-based walking speed estimation scheme for smartphone. A deep convolutional neural network (DCNN) is applied to automatically identify and extract the most effective features from the accelerometer and gyroscope data of smartphone and to train the network model for accurate speed estimation. Experiments are performed with 10 participants using a treadmill. The average root-mean-squared-error (RMSE) of estimated walking speed is 0.16m/s which is comparable to the results obtained by state-of-the-art approaches based on a number of body-worn sensors (i.e., RMSE of 0.11m/s). The results indicate that a smartphone can be a strong tool for walking speed estimation if the sensor data are effectively calibrated and supported by advanced deep learning techniques.Comment: 6 pages, 9 figures, published in IEEE Global Communications Conference (GLOBECOM

Machine Model Based Speed Estimation Schemes for Speed Encoderless Induction Motor Drives: a Survey

Speed Estimation without speed sensors is a complex phenomenon and is overly dependent on the machine parameters. It is all the more significant during low speed or near zero speed operation. There are several approaches to speed estimation of an induction motor. Eventually, they can be classified into two types, namely, estimation based on the machine model and estimation based on magnetic saliency and air gap space harmonics. This paper, through a brief literature survey, attempts to give an overview of the fundamentals and the current trends in various machine model based speed estimation techniques which have occupied and continue to occupy a great amount of research space

Modelling, control and sensorless speed estimation of micro-wind turbines for deployment in Antarctica

This paper presents the modelling, control and sensorless speed estimation of two micro-wind turbines deployed by the British Antarctic Survey (BAS) in Antarctica. Mathematical models for the generators attached to an Ampair 100 and Rutland 913 wind turbines and their experimental validation are given. Also, a model for the wind turbines, particularly taking into account the power coefficient Cp versus tip speed ratio λ relationship was proposed and successfully evaluated on a wind turbine emulator test rig. This paper describes an analogue speed estimator board and a Kalman filter for estimating the shaft speed. These estimators use only DC side measurements to match the characteristics of the current version of the turbine control board. The wind turbine control and speed estimators were tested on the emulator test rig using real wind data from BAS research bases in Antarctica. Using only DC side measurements leads to low computation requirements to execute the algorithms in comparison to commonly used schemes that rely on AC measurements. In addition, the estimation algorithms are based on the model of a PM generator connected to a diode rectifier, as they can be used in a wider range of applications, including DC to DC converters with MPPT algorithms based on speed measurements

Improved speed estimation in sensorless PM brushless AC drives

The application of flux-observer-based sensorless control to permanent-magnet brushless AC motor drives is described. Current methods of speed estimation are assessed, both theoretically and experimentally, and an improved method, which combines the best features of methods in which speed is derived from the differential of rotor position and from the ratio of the electromotive force to excitation flux linkage, is proposed. Its performance is verified experimentally

Adaptive Traffic Speed Estimation

Active traffic management aims to dynamically manage congestion based on existing and predicted traffic conditions. A challenge in this is that it is not usually possible to process data in real-time and use the output in control algorithms or in traveler information systems. A solution to this is to predict the traffic state based on assessments of current and past measurements. The work described in this paper develops an adaptive forecasting method to predict traffic speeds using dynamic linear models with Bayesian inference from a priori distributions. This study incorporates speeds collected from radar based sensors and validates the results with data collected from Bluetooth traffic monitoring technology. The highly adaptive model is confirmed with estimated traffic speeds during inclement weather and multiple incidents

Speed estimation applying sinc-filter to a period-based method for incremental position encoder

Precise speed estimation and short measurement time delay is compulsory for high-performance electric drives. Rotary incremental position encoders are widely used together with speed estimation techniques such as period-based, frequency-based, constant elapse time, and synchronous measurement methods. This paper proposes the speed estimation technique, which helps to obtain higher accuracy by means of filtering the results of period-based method using third order Sinc-filter. As the sequence of period-based method estimations has the properties of delta-sigma modulated signal, extra information can be extracted. For the specified measurement time that gives higher accuracy than moving average filter. The developed speed estimation method was tested using experimental setup showing better accuracy compared to conventional methods

MagSpeed: A Novel Method of Vehicle Speed Estimation Through A Single Magnetic Sensor

© 2019 IEEE. Internet of Things (IoT) is playing an increasingly important role in Intelligent Transportation Systems (ITS) for real-time sensing and communication. In ITS, the velocity of vehicles provides important information for traffic management. However, the present methods for monitoring vehicle speed have many shortcomings. In this paper, we propose MagSpeed, a novel vehicle speed estimation method based on a small magnetic sensor. The developed magnetic sensor system is wireless, cost-effective, and environmental-friendly. Through modelling of local magnetic field perturbations caused by a moving vehicle, we extract the characteristics of magnetic waveforms for speed estimation. In addition, we compare the performance of the models with other speed estimation algorithms, which shows the superior accuracy of the proposed technique in speed estimation

Evaluation of roadway spatial-temporal travel speed estimation using mapped low-frequency AVL probe data

© 2020 Elsevier Ltd The rapid increase in the number of vehicles equipped with GPS devices has resulted in using automatic vehicle location (AVL) data as probes to identify traffic flow status as well as route travel speed on a very fine spatial-temporal scale. However, these traffic monitoring approaches heavily rely on the widely distributed probe vehicles in the network and the high frequency of these probe samples, which are rarely implemented in the real world. This study aims to analyze the applicability of providing accurate traffic flow information from four types of low-frequency AVL data. Each data source is applied for speed estimation to develop guidelines on GPS data requirements for travel speed estimation. First, the probe sample size of each data source on each target corridor is studied to reveal the road segments that have the potential for speed estimation, along with the GPS sampling frequency of each data source. Second, the impact of probe vehicle types, sample sizes, and GPS sampling frequency is analyzed. This study offers guidance in using GPS data to conduct speed estimation in different scenarios, which can be further implemented in a prototype software tool for estimating the real-time travel speed. This study has shown the applicability for speed estimation from four types of GPS data, where the transit bus GPS data provides the best mean speed estimation. The speed estimation results are compared with loop detector data on a test road segment to evaluate its accuracy. The comparison results show that given the current GPS data sample size and updating frequency, the transit bus GPS data can provide a reasonably accurate estimation of the traffic flow speed with a mean absolute speed difference of 6.96 km/h