4 Best iPhone Accelerometer App

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If you are looking for the Best iPhone Accelerometer App to download for free, look no further.

Our expert panel of reviewers has tested dozens of apps and we have compiled a list of the 4 Best iPhone Accelerometer App that passed our tests.

Check out our list of the 4 Best iPhone Accelerometer App, tried and tested by our expert reviewers.

4 Best iPhone Accelerometer App

1. Accelerometer

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Accelerometer is an app which allows you to measure acceleration in all three axes in the most beautiful and understandable way ever!

• Plots realtime charts of acceleration
• Option to include or remove gravity from your measurements
• Export your data
• Logs maximum and minimum acceleration
• Adjustable sampling frequency (from 1 to 30 Hz)
• Interactive chart
• Line interpolation smoothing
• Very precise
• Measures from -20 to +20 G

Help us make Accelerometer even better by reviewing it. We’ll include your feature requests in future updates.

2. Physics Toolbox Accelerometer

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“This accelerometer sensor app measures and displays a graph of G-Force vs. Time (s) and Acceleration (m/s/s) vs. Time (s) in x, y, and/or z dimensions, as well as total magnitude.

G-Force data can be recorded and exported as a .csv attachment using a comma as a delimiter.

This app can be especially useful for monitoring changes in acceleration in vehicles or airplanes, and to monitor vibrations of any sort. This app can be used in the classroom to hep students quantify activities with Newton’s 2nd Law by taking a mobile device with the app on field trips to amusement park rides, roller coasters, or even in elevators for standard “elevator problems.” Because G-Force is the ratio of normal force / weight of the object in question, students can the known mass of the object in question to quantitatively draw force diagrams throughout the object’s motion.”

3. SensorLog

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“With SensorLog you can read out sensor data of your iPhone, iPad and Apple Watch.

The sensor data (csv or JSON format) can be saved to file, streamed via TCP/UDP, or send as HTTP GET/POST request.

iPhone und iPad Logging:

– Sensor data can be sampled with up to 100Hz (depending on the version of your iOS device and fore or background mode).

– Sensor data can be streamed in server or client mode supporting TCP and UDP protocol. Streaming is supported up to 100Hz depending on the network speed and the receiving client or server configuration.

– Via HTTP(S) GET/POST request sensor data can be sent in JSON (POST) or form-url encoded (GET and POST) format to a REST API. Up to 20Hz upload rate is supported depending on the network speed and receiving server configuration.

Apple Watch Logging:

– Logging Option 1: Logging duration up to 1 hour
Supports logging of all selectable sensor data with up to 50Hz in background. Higher sampling rates up to 100 Hz are possible by selecting less sensors. Streaming (only client modus, TCP) and HTTP requests are supported.

– Logging Option 2: Logging duration greater 1 hour
While in background only logging of accelerometer data with up to 50Hz is supported. In foreground logging of all sensors with up to 100Hz is supported. Streaming and HTTP requests are only supported in foreground.

– Streaming is supported up to 100Hz depending on the network speed, the receiving server configuration, and chosen logging option. A coupled iPhone with LAN/WAN access is required

– Via HTTP(S) GET/POST request sensor data can be sent in JSON (POST) or form-url encoded (GET and POST) format to a REST API. Up to 10Hz upload rate is supported depending on the network speed and receiving server.

The following data of the iOS framework (iPhone, iPad) is provided by SensorLog (depending on the device version):
– CLLocation: latitude, longitude, altitude, speed, course, verticalAccuracy, horizontalAccuracy, floor (please note: this is not GPS raw data!)
– CLHeading: heading.x, heading.y, heading.z, trueHeading, magneticHeading, headingAccuracy
– CMAccelerometer: acceleration.x, acceleration.y, acceleration.z
– CMGyroData: rotationRate.x, rotationRate.y, rotationRate.z
– CMMagnetometerData: raw magneticField.x, magneticField.z, magneticField.z
– CMDeviceMotion: yaw, roll, pitch, rotationRate, userAcceleration, attitudeReferenceFrame, quaternions, gravity, magneticField, heading, magneticField.accuracy
– AVAudioRecorder: peakPower, averagePower (decibels)
– Core ML Model output (supported type int, double, string, dictionary)
– CMMotionActivity: Activity, activity.startDate, activity.confidence
– CMPedometer: numberOfSteps, startDate, distance, endDate, pedometerAverageActivePace, pedometerCurrentPace, pedometerCurrentCadence,
floorsAscended, floorsDescended
– CMAltimeter: relativeAltitude, pressure
– logging of WIFI and network carrier IP addresses
– logging of the device orientation
– logging of battery level
– labelling of the logged data

On the Apple Watch SensorLog supports logging of the following data:
– CLLocation: latitude, longitude, altitude, speed, course, verticalAccuracy, horizontalAccuracy, floor
– CMAccelerometer: acceleration.x, acceleration.y, acceleration.z
– CMDeviceMotion: yaw, roll, pitch, rotationRate, userAcceleration, attitudeReferenceFrame, quaternions, gravity, magneticField, heading, magneticField.accuracy
– CMMotionActivity: Activity, activity.startDate, activity.confidence
– CMPedometer: numberOfSteps, startDate, distance, endDate, floorsAscended, floorsDescended
– CMAltimeter: relativeAltitude, pressure
– Battery information

Machine Learning:
SensorLog supports Core ML models created with Apple’s Create ML app. Simply log data with SensorLog, train a model on the data in Create ML, load the exported model in SensorLog, and log its prediction with SensorLog. Supported Create ML models are: Activity Classifier, Tabular Regressor, and Tabular Classifier.”

4. Vernier Graphical Analysis GW

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“Graphical Analysis™ GW is a tool for science students to collect, graph, and analyze data from Vernier Go Wireless® sensors.

Sensor data-collection support:
• Vernier Go Wireless® Temp and Go Wireless pH sensors
• Vernier Go Wireless Heart Rate and Go Wireless Exercise Heart Rate monitors
• Vernier LabQuest® sensors used with Go Wireless Link or LabQuest Stream interfaces
• Basic support for some Vernier Go Direct® Sensors
• NODE (by Variable Inc.) sensor support

Additional experiment options:
• Data Sharing via Wi-Fi connection to LabQuest 2 or Logger Pro® 3
• Built-in Sensors (accelerometers)
• Manual Entry

Note: Sensor data collection and Data Sharing require the purchase of hardware from Vernier Software & Technology. Built-in sensor and manual entry of data can be performed without a hardware purchase. For more information on Data Sharing, visit http://www.vernier.com/css

Key Features – Data Collection
• Multi-sensor data-collection support
• Time Based, Event Based, and Drop Counting data-collection modes
• Configurable data-collection rate and duration for time-based data collection
• Sensor calibrations
• Option to zero and reverse sensor readings
• Graph match feature for use with motion detectors
• Manual entry of data from keyboard and clipboard

Key Features – Data Analysis
• Display one, two, or three graphs simultaneously
• View data in a table or show a graph and table side-by-side
• Draw Predictions on a graph to uncover misconceptions
• Examine, interpolate/extrapolate, and select data
• Apply Statistics calculations to find mean, min, max, and standard deviation
• Perform curve fits, including linear, quadratic, natural exponent, and more
• Add calculated columns based on existing data to linearize data or investigate related concepts
• Pinch to scale graphs

Key Features – Collaboration and Sharing
• Add graph titles
• Export graphs and data for printing and inclusion in lab reports
• Export data in .CSV format for analysis of data in spreadsheet software such as Excel®, Google Sheets™, and Numbers®

Vernier Software & Technology has over 35 years of experience in providing effective learning resources for understanding experimental data in science and math classrooms. Graphical Analysis GW is a part of the extensive system of sensors, interfaces, and data-collection software from Vernier for science and STEM education.”