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.
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!
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!
The EdaMove sensor emerges fresh from the lab with its version 3 upgrade complete. The same high quality data you’ve come to trust and respect, now capable of interacting with our cloud based experience sampling platform movisensXS.
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.
Now it’s finally possible… capturing Experience Sampling data in sync with physiological parameters!
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…
We've just added a few white papers to our website that we thought you may find interesting.
Energy Expenditure
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.
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.
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.
We took our acclaimed accelerometer technology and added 5 channels of light detection (Red, Green, Blue, Clear, IR) to develop our newest solution for ambulatory assessment: Light and Activity Sensor LightMove 3
Capable of recording ambient light, 3D acceleration, temperature, and barometric air pressure data for up to two months, our sensor is ideal for research into the effect of light exposure and physical activity in the fields of:
Depression
Sleep analysis
Circadian rhythms
Shift work
Behavioral monitoring
Featuring:
The ability to easily calculate parameters like activity class, steps, energy expenditure, light irradiance or illuminance
Integrated Bluetooth Smart allowing live transmission of data calculated on the sensor
Dr. Verkuil & Dr. Brosschot from Leiden University will use heart rate variability (HRV) to investigate the impact of psychological stress on physiological stress. It is therefore important to measure the psychological stress at moments when people are also likely to be physiologically stressed. In their project, all periods of HRV that are not explained by physical activity will be detected. Each non-metabolic induced HRV decrease period will trigger a smartphone app that asks subjects to indicate what they are currently thinking and doing and how they are currently feeling. The aim of this study is to test if stress cognition is increased during periods of HRV decrease compared to random neutral episodes. Leiden University realizes this highly innovative project using movisens ecgMove 3 and movisensXS.
