Sensor Of Smartphone Research Articles

Measuring velocity and acceleration of a moving object in linear motion using proximity sensor of smartphone

This paper describes an alternative method for measuring the time or time interval of a moving object in linear motion using a smartphone’s proximity sensor. Two smartphones are stationary, placed on an inclined plane, and measure the time or time interval of a moving object (with attached a cardboard) passing through the sensor using the Phyphox app. The obtained data are used to calculate both velocity and acceleration for the moving object. This experiment is easy to set up with readily available objects and prevents damage to the smartphones.

A smartphone-based multi-wavelength photometer for on-site detection of the liquid colorimetric assays for clinical biochemical analyses

A novel portable and cost-effective multi-wavelength smartphone colorimetric reader (MW-SCR) was designed for the quantitation of liquid colorimetric assays. Six light-emitting diodes (LEDs) with different wavelengths were assembled in a roulette fashion to form the light detection source. An ambient light sensor of smartphone was applied as the detector. Using this MW-SCR, we detected six common serum analytes, including alkaline phosphatase(ALP), potassium(K+), chloride(Cl−), alanine aminotransaminase(ALT), glycated serum protein(GSP), and albumin, to evaluate the feasibility and reliability of the MW-SCR. Strong linear regression (R2>0.99) analysis with a wide linear range and good limits of detection were found for this device, detection limits as low as 0.37 U/L, 0.0083 mmol/L, 0.78 mmol/L, 4.49 KarU, 0.023 mmol/L and 2.23 g/L for ALP, K+,Cl−, ALT, GSP and albumin, respectively, were achieved. It also had high repeatability that ranged from 0.20 to 0.61 % relative standard deviations (RSD), and the precision ranged of MW-SCR were from 0.26 to 1.17 % RSDs. Results demonstrated that our device showed remarkable stability, accuracy, and repeatability, presenting excellent consistency with a professional microplate reader. It can be redesigned for multiple indications, such as clinical biochemical analyses, food safety testing, environmental monitoring and so on, because it is open source, user friendly and easy manufacturing.

AI Assisted Human Activity Recognition (HAR)

Human Activity Recognition and assisting user on the basis of his context is attracting researchers since decade Researchers are working in the area to increase the accuracy of detection by various means. The challenging issue is to determine the correct supervised classifier for the detection purpose. This paper intent to examine the methodology used to recognize HAR and the impact of classifiers practiced in training and Testing. We have also tried to identify the suitable supervised machine learning model for HAR. Data of 30 Users with 561 features belonging to accelerometer and gyroscope sensor of smartphone from UCI repository is used for evaluation purpose. Nine different supervised machine learning Models are trained and tested on the dataset. The result concludes that HAR is a process which depends upon the classifiers used. It also conclude that out of 9 different Machine learning models ANN performs well and after that SVM, kNN, Random Forest and Extra Tree are equally good models for the purpose of HAR with Accuracy and execution time as the performance evaluation metric.

Reliability of smartphone measurements of vital parameters: A prospective study using a reference method

ObjectiveIn this study, we aimed to evaluate the accuracy of HR and SaO2 data obtained using a smartphone compared with the measurements of a vital signs monitor (VSM) and an arterial blood gas (ABG) device, respectively. Material and methodsIn this single-center prospective study, the HR and SaO2 measurements were performed using the built-in sensor and light source of a Samsung Galaxy S8 smartphone and compared to the results of VSM and ABG device. The Bland-Altman analysis was used to evaluate and visualize the agreement between the methods. ResultsThe data of 101 patients were analyzed. There was a high correlation between HR measured by smartphone and HR measured by VSM [P < 0.0001; 0.9918 (95% CI = 0.987–0.994)]. In addition, the SaO2 values obtained by smartphone were highly correlated with those by ABG (P < 0.0001; 0.968 (95% CI = 0.952–0.978)). ConclusionThe HR and SaO2 values obtained by smartphone were found to be consistent with the measurements of the reference devices. With the growing use of smartphone technology in the health field, we foresee that patients will be able to make their own triage assessment before presenting to the hospital.

Smartphone Heading Correction Based on Gravity Assisted and Middle Time Simulated-Zero Velocity Update Method.

Electronic appliances and ferromagnetic materials can be easily found in any building in urban environment. A steady magnetic environment and a pure value of geomagnetic field for calculating the heading of the smartphone in case of pedestrian walking indoors is hard to obtain. Therefore, an independent inertial heading correction algorithm without involving magnetic field but only making full use of the embedded Micro-Electro-Mechanical System (MEMS) Inertial measurement unit (IMU) device in the smartphone is presented in this paper. Aiming at the strict navigation requirements of pedestrian smartphone positioning, the algorithm focused in this paper consists of Gravity Assisted (GA) and Middle Time Simulated-Zero Velocity Update (MTS-ZUPT) methods. With the help of GA method, the different using-mode of the smartphone can be judged based on the data from the gravity sensor of smartphone. Since there is no zero-velocity status for handheld smartphone, the MTS-ZUPT algorithm is proposed based on the idea of Zero Velocity Update (ZUPT) algorithm. A Kalman Filtering algorithm is used to restrain the heading divergence at the middle moment of two steps. The walking experimental results indicate that the MTS-ZUPT algorithm can effectively restrain the heading error diffusion without the assistance of geomagnetic heading. When the MTS-ZUPT method was integrated with GA method, the smartphone navigation system can autonomously judge the using-mode and compensate the heading errors. The pedestrian positioning accuracy is significantly improved and the walking error is only 1.4% to 2.0% of the walking distance in using-mode experiments of the smartphone.

Location Privacy Violation via GPS-agnostic Smart Phone Car Tracking

Smart phones nowadays are equipped with global positioning systems (GPS) chips to enable navigation and location-based services. A malicious app with the access to GPS data can easily track the person who carries the smart phone. People may disable the GPS module and turn it on only when necessary to protect their location privacy. However, in this paper, we demonstrate that an attacker is still able to track a person by using the embedded magnetometer sensor in victim's smart phone, even when the GPS module is disabled all the time. Moreover, this attack neither requests user permissions related to locations for installation, nor does its operation rely on wireless signals like WiFi positioning or suffer from signal propagation loss. Only the angles of a car's turning measured by the magnetometer sensor of a driver's smart phone are utilized. Without loss of generality, we focus on car tracking, since cars are popular transportation tools in developed countries, where smart phones are commonly used. Inspired by the intuition that a car may exhibit different turning angles at different road intersections, we find that an attacker can match car turning angles to a map to infer the actual path that the driver takes. We address technical challenges about car turn angle extraction, map database construction, and path matching algorithm design to make this attack practical and efficient. We also perform an evaluation using real-world driving paths to verify the relationship between the numbers of turns and the time cost of the matching algorithm. The results show that it is possible for attacker to precisely pinpoint the actual path when the driving path includes 11 turns or more. More simulations are performed to demonstrate the attack with lager selected local areas.

A real-time fall detection system based on the acceleration sensor of smartphone

Falls are a major problem for the elderly people leading to injury, disability, and even death. In this article, we propose a fall detection system that uses the acceleration sensor of a smartphone to detect a senior citizen falling in real time and to use the communication capabilities of the smartphone to notify the administrator of such an event. The proposed system conducts real-time location tracking using Google’s Map and 3D information to cope with a critical situation and to make an urgent intervention in an emergency. Since Google’s 3D mapping service can provide information on the surrounding buildings and their shapes with more precision than a 2D service or text-based service, it is possible to ensure a prompt response for senior citizens when such location information is available. To verify the validity and efficiency of this article, we measured the change in acceleration sensor value by classifying the subjects by age group and gender. As a result, we found that the signal vector magnitude value of the acceleration sensor value variation showed a great difference in daily life such as walking, running, sitting, and falling.

HybridBaro: Mining Driving Routes Using Barometer Sensor of Smartphone

Recent research showed that human mobility is characterized by reproducible patterns, i.e., humans tend to travel a few known places. Timely identification of these significant journeys has prospects for emerging intelligent applications like real-time traffic route recommendation and automated HVAC systems. Existing mobile systems, however, utilize energy-hungry sensors like GPS and gyroscope to detect significant journeys, which make it hard to keep such systems running to continuously monitor driving routes. To address this issue of energy efficiency without compromising the performance, in this paper, a hybrid mobile system based on the barometer sensor of a smartphone is developed. Distinctive elevation signatures of driving routes are captured using the smartphone barometer sensor that is exceptionally energy-efficient and position/orientation-independent. Degraded accuracy due to flat areas with minimal elevation changes is offset by developing an adaptive algorithm that opportunistically obtains GPS locations for a very short period of time when such flat areas are detected in real time. Using over 150 miles of field data, it is demonstrated that the proposed mobile system achieves the mean detection accuracy of 97% with the mean false positive rates of 1.5%.

Finding the average speed of a light-emitting toy car with a smartphone light sensor

This study aims to demonstrate how the average speed of a light-emitting toy car may be determined using a smartphone’s light sensor. The freely available Android smartphone application, ‘AndroSensor’, was used for the experiment. The classroom experiment combines complementary physics knowledge of optics and kinematics to find the average speed of a moving object. The speed of the toy car is found by determining the distance between the light-emitting toy car and the smartphone, and the time taken to travel these distances. To ensure that the average speed of the toy car calculated with the help of the AndroSensor was correct, the average speed was also calculated by analyzing video-recordings of the toy car. The resulting speeds found with these different methods were in good agreement with each other. Hence, it can be concluded that reliable measurements of the average speed of light-emitting objects can be determined with the help of the light sensor of an Android smartphone.

A Real-time Fall Detection System Based on the Acceleration Sensor of Smartphone

Falls are a major problem for the elderly people leading to injury, disability, and even death. In this article, we propose a fall detection system that uses the acceleration sensor of a smartphone...

Wi-Fi 환경에서 센서 및 정규분포 확률을 적용한 실내 위치추정 알고리즘

In this paper, the localization algorithm for improving the accuracy of the positioning using the Wi-Fi fingerprint using the normal distribution probability and the built-in typed accelerometer sensor, the gyroscope sensor of smartphone in the indoor environment is proposed. The experiments for analyzing the performance of the proposed algorithm were carried out at the region of the ho rizontal and vertical 20m * 10m in the engineering school building of our university, and the performance of the proposed algorithm is compared with the fingerprint and the DR (dead reckoning) while user is moving according to the assigned region. As a result, the maximum error distance in the proposed algorithm was decreased to 2cm and 36cm compared with two algorithms, respectively. In addition to this, the maximum error distance was also less than compared with two algorithms as 16.64cm and 36.25cm, respectively. It can be seen that the fingerprint map searching time of the proposed algorithm was also reduced to 0.15 seconds compared wi th two algorithms.

Development of the obstacle detection system combining orientation sensor of smartphone and distance sensor.

In the existing walking support system, the range of detection is limited, and it is very difficult to detect obstacles such as a step or a hollow. Therefore, we aim at the development of a new walking support system for a visually-impaired person detecting neighboring obstacles by making use of a smartphone which is a high-performance portable information terminal. In this study, we have developed a walking support system combining the orientation sensor of the smartphone and a small distance sensor at the first stage. When we investigated its precision in detecting an obstacle, it was found that the system could detect obstacles which disturbed the walking of a visually-impaired person such as a step and a wall. We will find an appropriate key to detection to improve the detection precision of the system and make further improvements in the system by trial experiments in the future.

Toward Developing a Computational Model for Bipedal Push Recovery–A Brief

The human being can negotiate with external push up to certain extent reactively. Grown up persons have better push recovery capability than kids and also the professional wrestlers acquire better push recovery capability than normal human being. The acquired push recovery capability, therefore, is based on learning. However, the mechanism of learning is not known to us. Researchers around the world are trying to explore this mystery through developing various models and implementing them on various humanoid robots. All the models based on conventional mechanics and controls have inherent limitations. We believe appropriate computational model based on learning will be able to effectively address this issue. Accordingly, we have collected extensively humanoid push recovery data using our innovative idea of exploiting the accelerometer sensor of smart phone. Through our experiments, we have studied the human push recovery by fusing data at feature level using physics toolbar accelerometer of android interface kit. The subjects for the experiments were selected both as right handed and left handed. Pushes were induced from the behind with close eyes to observe the motor action as well as with open eyes to observe learning-based reactive behaviors. A learning vector quantization-based classifier has been developed to identify the coordination between various push and hip and knee joints.

스마트폰 센서 기반 상황인식 시스템 연구 및 설계

This paper describes the design and implementation of situation recognition system with smart phone sensors, which recognizes the dangerous situation at anytime, anywhere through intuitive data analysis of the combination of the sensor. The implemented system consists of wearable heart rate sensor and acceleration sensor of smart phone instead of existing sensor that is attached to the body. It is also designed to get more effective results of recognition about the dangerous situation using merged displacement values of acceleration sensor and heart rate sensor which are measured in the process of recognizing dangerous situations. This research, in accordance with the wide penetration of smartphones, achieves the fast status determination through the combination of an acceleration sensor and a heart rate sensor applied to its own status perception algorithm for anyone who needs the stable perception of risk without the need for a separate provision of the sensor.

개선된 Google Activity Recognition을 이용한 상황인지 모델

Activity recognition technology is gaining attention because it can provide useful information follow user’s situation. In research of activity recognition before smartphone’s dissemination, we had to infer user’s activity by using independent sensor. But now, with development of IT industry, we can infer user’s activity by using inner sensor of smartphone. So, more animated research of activity recognition is being implemented now. By applying activity recognition system, we can develop service like recommending application according to user’s preference or providing information of route. Some previous activity recognition systems have a defect using up too much energy, because they use GPS sensor. On the other hand, activity recognition system which Google released recently (Google Activity Recognition) needs only a few power because it use ‘Network Provider’ instead of GPS. Thus it is suitable to smartphone application system. But through a result from testing performance of Google Activity Recognition, we found that is difficult to getting user’s exact activity because of unnecessary activity element and some wrong recognition. So, in this paper, we describe problems of Google Activity Recognition and propose AGAR(Advanced Google Activity Recognition) applied method to improve accuracy level because we need more exact activity recognition for new service based on activity recognition. Also to appraise value of AGAR, we compare performance of other activity recognition systems and ours and explain an applied possibility of AGAR by developing exemplary program.

Analyzing Driving and Road Events via Smartphone

ABSTRACT Travelling in India by road is considered dangerous as traffic conditions are chaotic, the drivers drive recklessly, and the roads are poorly maintained. Therefore, there is need to monitor driver behavior and road condition regularly. In this work, we developed a method that collects data using accelerometer sensor present in Smartphone and analyze driving events and road anomalies. This paper presents the patterns of driving events, such as left turn, right turn, sudden braking and sudden forward acceleration, and road conditions, such as pothole, bump and rough patch, detected in the data set. This work will help in analyzing driving behavior and road anomalies to ensure driver safety and maintenance of roads. General Terms Driving Events, Road Anomalies Keywords Left Turn, Right Turn, Sudden Braking, Sudden Forward Acceleration, Pothole, Bump and Rough patch. 1. INTRODUCTION Although there are various transport modes but road transportation mode still plays a major role in people lives. In this busy world all people in hurry to reach their destination as quickly as possible which further leads them to drive fast, recklessly, without obeying the traffic rules which further lead them to accidents sometimes also to their loss of lives. In India potholes, shards of glass, speed breakers, cow dung, garbage, mud pits and many other things that may be found on the road and also less attention is given to the maintenance of roads. So there arises the need to monitor the driving style and road anomalies to ensure driver safety and for the maintenance of roads. Previously a lot of work is done in this field but researchers mainly focused on monitoring either driver behavior or road conditions using specialized hardware deployed inside the car [1] [2] [3] [4] [5] [6] or roadside which is expensive and also requires maintenance. Our method, contrary from previous work, use accelerometer sensor of Smartphone to observe both the pattern of driver’s driving style and road anomalies. Smartphone based approach is considered good as it is inexpensive, portable, requires less maintenance and also relying to the fact that many people already own it. This paper is organized as follows, section 2 discusses already present techniques for detecting driving events detection and road anomalies, section 3 contains experimental setup for data collection followed by data preprocessing , section 4 discusses the different patterns analyzed of various driving events and road anomalies, and section 5 provides the conclusion.

An efficient character input scheme with a gyro sensor of smartphone on ubiquitous cluster computing

Recently, smartphones have rapidly become popular and research has been actively conducted on methods of making the character inputs that are most frequently used more convenient and faster. Howeve...

Location-Based Service (LBS) Falling-Detection and Alarm System by Smartphone

Up to date, smart phone with built-in sensors (tri-accelerometer, GPS) and Internet access has become a useful kit for many purpose using. In this paper, we present the design and the implementation a combine approach for the detection of falls by smartphone. It used analyzing of body postures, mobility and falling location to recognize falls happened. In this paper, we present a system with real-time classification of human movements based on built-in sensor of smartphone. At the same time, it is able to classify the data for falling recognition. In our algorithm, it is not only to analysis of body postures by tri-accelerometer, but also to analysis of falling location by GPS sensor. We used a parameter called falling-risk (FR) to measure falling risk at different location. The FR is based on falling history at GPS location (x, y). It will alarm falling with fall location when falling and it will notice when user entered the high risk area. The major advantage of the proposed system is the novel application of smartphone which already have the necessary sensors and can monitor fall ubiquitously without any additional devices. We develop an Android application that monitors the phone’s built-in sensors, detects falls, and generates high risk alerts when user passes through the high risk area. We test the application against a number of Activities of Daily Life (ADLs) and fall events, and present the results.