If you're working on a new study incorporating mutliple data sets per participant, you'll need to merge all the data sources in a meaningful way for optimal statistical analysis.
The team from movisens now offer the DataMerger - a software tool that synchronises and merges data from different sources.
Click here to learn more about the DataMerger
The newest movisens SensorManager provides all the resources for you to optimize your movisens sensor. In addition to the standard functionality, we've included the ability to update your sensors to their latest firmware version, an essential upgrade for those who'd like to take advantage of the sensor triggering capability of movisensXS.
After the great success of the Move 4 and EcgMove 4, we've incorporated the same suite of innovative improvements to bring you the EdaMove 4 and the LightMove 4.
Click here to see the entire range.
Finally we're able to unveil the new sensor generation. We're starting the launch of series 4 with the Move 4 and the EcgMove 4. With the 4th generation, movisens took into account the numerous requirements of researchers, implementing their wishes and also further increasing the quality of the sensors.
On the 12th of July the newest generation of our sensors shall emerge from the lab. So, save that date in your calendar and be one of the first who benefits from the newest developments and upgrades to our sensor range!
You can't wait until the 12th of July?
Thanks to our recent relocation we now have the space to grow our team, and also to offer training here at our own premises! Our new office space allows us to offer seminars in the field of ambulatory assessment/mobile monitoring to researchers and research groups. This allows research teams to undertake basic training in the best methods for gathering physiological and/or experience sampling data and provides a great opportunity to clarify all the important issues they may face in their upcoming study. The training includes both a theoretical and practical component.
If you or your research group would like to attend a training seminar, please contact us at info@movisens.com. We will announce upcoming dates for seminars here on our news page, and via our newsletter which you can subscribe to below. On average we send only one newsletter per month, so it shouldn't hurt your inbox too much!
Our very own Florian Richert crafted a fascinating white paper about the difficulties of accurately assessing energy expenditure using actigraphy. He details the history of interpreting accelerometer data, highlighting the advantages of a transparent method of calculation and the importance of capturing raw data in the sensor. Focusing on the quest to accurately estimate energy expenditure, Florian delves in to the existing research and reveals the weaknesses in the current approach and the way forward to develop a sustainable method of calculating parameters from accelerometer data as newer and better algorithms are devised.
The paper carves through the different existing methodologies and presents solid recommendations for integrating high quality accelerometer data in future research. In particular, utilising physiological data to assist in capturing psychological data in an ambulatory setting.
We hope you enjoy it.
Click here to download
Previously discovering any relationships between physiological and subjective data would require cross referencing the information after the study. What if we said you can now detect heart rate and have it trigger a form? Or even psychological stress? Or you’d like to track Activity Levels? We’ve got you covered with that and more…
To learn how sensor triggering can unlock greater insights in your research visit our stand at the following conferences:
We've just added a few white papers to our website that we thought you may find interesting.
First is a validation paper on the energy expenditure calculation of the Move II (now superceded by the Move 3). Some manufacturers are still utilising basic linear regression models to calculate the energy expeniture in their activity sensors. This paper demonstrates the advantages of utilising an activity class based algorithm vs. a basic linear regression to obtain a more accurate estimation of energy expenditure. It's somewhat old news, but it's good to remind everyone occasionaly of the superiority of our activity based method over the basic linear regression model.
White Paper Move II Validation Energy Expenditure
Secondly we have a validation of the R-peak detection capabilities of the EkgMove (now superceded by the EcgMove 3). This paper puts the EkgMove through it's paces by introducing movement artefacts and validating the detection capabilities in reference to the Somnoscreen plus.
White Paper Validity of R-Peak Detection
The measurement of arousal using Electro Dermal Activity (Galvanic Skin Response) is often hindered by factors like movement, temperature and exercise. This paper highlights the benefits of capturing activity data in addition to EDA/GSR. It details a method that can isolate the emotional arousal component of EDA/GSR using our EdaMove sensor.