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First is a validation paper on the energy expenditure calculation of the Move II (now superceded by the Move 3).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Secondly we have a validation of the R-peak detection capabilities of the EkgMove (now superceded by the EcgMove 3).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 use heart rate variability (HRV), to investigate the impact of psychological stress on physiological stress. It is therefore important to measure psychological stress at moments when people are likely 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 and movisensXS.
Das EU-Projekt MIRROR untersucht die Unterstützung von reflektiven Lernprozessen am Arbeitsplatz. Im Rahmen dieses Projekts wird der ekgMove der movisens GmbH zur Messung kardiologischer Parameter und der Aktivität eingesetzt.
Die Erfassung körperlicher Aktivität kann auf relativ einfache und praktikable Art anhand von Akzelerometern erfolgen. Auf der Grundlage erfasster Beschleunigungswerte kann durch spezielle Algorithmen der Energieumsatz errechnet werden. Um die Messgenauigkeit des kmsMove zu überprüfen, wurden zwei unterschiedliche Validierungsstudien vom Sportinstitut des KIT in Kooperation mit hiper.campus durchgeführt.
Im Rahmen einer Kooperationsstudie zwischen hiper.campus und dem Sportinstitut des KIT wurde der Aktivitätssensor der movisens GmbH mittels spirometrischen Messungen validiert.
Die Analyse der Daten zeigt, dass die Intra-Klassen-Korrelation zwischen dem Aktivitätssensor und der indirekten Kalorimetrie bei einer Messdauer von 100 Minuten 0.82 (0.38–0.96; p = 0.003) beträgt und bei einer Messdauer von 7 Stunden im Durchschnitt 0.81 (0.22–0.97; p = 0.01). Die Ergebnisse bestätigen, dass der movisens Aktivitätssensor mit hoher Genauigkeit den Energieumsatz bei Reha- Patienten bestimmt und ein ideales Gerät zur Ermittlung und Analyse alltäglicher Bewegungen darstellt.
Estimation of energy expenditure using accelerometers and activity-based energy models - validation of a new device
