Description

With SensorLog you can read out sensor data of your iOS and watchOS device and save it as CSV or JSON file. Sensor data of your iOS device can be sampled with up to 100Hz (depending on the version of your iOS device and fore or background mode).

On iPhone and iPad 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 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.

SensorLog supports logging to file, streaming via tcp/udp, and sending HTTP requests while it is running in the background.

On the Apple Watch sampling rates up to 100Hz are supported.

Machine Learning: Apple CoreML models can be loaded, sensor data be mapped to model input features, and model output being logged.

The following data of the iOS framework (iPhone, iPad) is provided by SensorLog:
- CLLocation: latitude, longitude, altitude, speed, course, verticalAccuracy, horizontalAccuracy, floor
- 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, magneticField.accuracy
- AVAudioRecorder: peakPower, averagePower (decibels)
- Core ML Model output (supported type int, double, string, dictionary)

iPhone 5S and following:
- CMMotionActivity: Activity, activity.startDate, activity.confidence
- CMPedometer: numberOfSteps, startDate, distance, endDate

iPhone 6 / iPhone 6 Plus and following:
- CMPedometer: numberOfSteps, startDate, distance, endDate, pedometerAverageActivePace, pedometerCurrentPace, pedometerCurrentCadence,
floorsAscended, floorsDescended
- CMAltimeter: relativeAltitude, pressure

SensorLog additionally supports:
- logging of WIFI and network carrier IP addresses
- logging of the device orientation
- logging of battery level
- tagging of the logged data with numerical values

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, magneticField.accuracy
- CMMotionActivity: Activity, activity.startDate, activity.confidence
- CMPedometer: numberOfSteps, startDate, distance, endDate, floorsAscended, floorsDescended
- CMAltimeter: relativeAltitude, pressure
- Battery information

What’s New

Version 3.2

Bug Fix UI

Ratings and Reviews

4.3 out of 5
16 Ratings

16 Ratings

Npvw ,

Works out of the box

Worked as expected and very easily.
I cannot say yet if it is accurate but was able to log data to a file and email it without hassle.
I need to just make sense of what the state options mean? Bernd can you explain perhaps? 0-5?

Good and simple app.

Developer Response ,

Thanks for your feedback. With the state option ( or label in Version 2.3 ) you can tag the recorded sensor data. The state ( label ) information can be of benefit if you want to tag your data for later post processing as for instance if you want to use it for machine learning tasks. For example use state / label „1“ while walking and label „2“ while jumping. Later on you can easily identify the „jump“ and „walk“ data in your log by the label column.

Lumberton NJ ,

Output File Time Tags worthless

App measurement data collection is easy to initiate but limitations in the output measurement file time tags make the data useless for performing engineering analysis. I am using it to log time tagged earth magnetic field measurements. I tried to log the data at 100 hz but when I inspected the data file the time tags only have 1 decimal point of accuracy (0.1 sec) and there are multiple rows of data with the same time tag; thus when I collect data at say 100 hz, the data entries are not exactly 100 hz but some mismatch of approx 100 hz.

Any way I can force the output to be exactly the hz I want.

Also can you increase the time tag accuracy down to at least the hz you are collecting data at (e.g. If 100 hz data collection, then time tags accurate to 0.01 sec).

Without these fixes to you app, your app is worthless for engineers using it for engineering applications.

Developer Response ,

Hi Lumberton, please double check to correctly import the logged data. Depending on the program you use to read the log file you may have data conversion / transformation issues (for instance that may happen if you import the data in MS Excel without using the data import functionality and not defining the correct data types). For detailed information on the different sensor timestamps and therewith connected sampling rates please have a look at the explanations and links provided under the Configuration -> Info view of the app. Please feel free to contact me via email sensorlog@berndthomas.net. I would appreciate to help you via email.

Bogidawn ,

Great! ARKit?

Awesome app for raw sensor data. However now with AR we have access to an additional, very awesome metric: accurate positional data in a world scene. It would be super cool if one of these sensor-logging apps supported it.

Information

Seller
Bernd Thomas
Size
3.1 MB
Category
Utilities
Compatibility

Requires iOS 12.4 or later. Compatible with iPhone, iPad, and iPod touch.

Languages

English, German

Age Rating
Rated 4+
Location
This app may use your location even when it isn't open, which can decrease battery life.
Copyright
© Bernd Thomas
Price
$4.99

Supports

  • Family Sharing

    With Family Sharing set up, up to six family members can use this app.

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