Category: Publications

Validity and reliability of psychophysiological data from wearable devices

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With an uncompromising focus on quality and reliability, we've remained the first choice for the discerning researcher who requires the best data collection and analysis tools for their work.

We're delighted to report the publication of one of our esteemed customers:
From lab to life: Evaluating the reliability and validity of psychophysiological data from wearable devices in laboratory and ambulatory settings. Behavior Research Methods, 1-20. Hu, X., Sgherza, T. R.,Nothrup, J. B., Fresco, D. M., Naragon-Gainey, K., & Bylsma, L. M. (2024).


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Ecological Momentary Assessment

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How do affect, diet and activity level influence binge eating?
The recent study from Bridgette Do from the University of Southern California "Associations among affect, diet, and activity and binge-eating severity using ecological momentary assessment in a non-clinical sample of middle-aged fathers" investigated those associations using Ecological Momentary Assessment.
Find out how this study was enabled by our experience sampling solutions!

The meaning of Move 4 as Inertial measurement units (IMUs)

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Pervasive healthcare is among the most prominent fields of research with an increasing demand for IMUs.
Inertial measurement units (IMUs) are electronic devices that typically consist of a 3-axis accelerometer (which measures linear acceleration) and a 3-axis gyroscope (which measures angular velocity).
Typically, IMUs are used to track movement patterns or recognize activities of the user like gait analisis.

The newly published paper (How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications) shows that also in gait analysis the sensor Move 4 of movisens supplies outstanding results!

IMU device specification of the Move 4

Onboard Memory

4GB

Battery Capacity

380mAh

Max. Sampling Rate

Max. sampling rate can be customized to 256 Hz

Accelerometer Range

±16 g

Gyroscope Range

±2000 deg/s

Additional Sensors

Barometer

Temperature Sensor

Ambient Light Sensor (LightMove 4)

IR Sensor (Customizing: Integration of an IR temperature sensor)

Respiration Sensor (Customizing: Integration of an respiration sensor)

Charging Options

Micro USB

Additional Adaptor/Dock

Waterproof

IP64

Developer Options

Java API

The importance of accelerometry and gyroscope for eating behaviour and associated intake

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The article "OREBA: A Dataset for Objectively Recognizing Eating Behaviour and Associated Intake“ (Rouast et al., 2020) shows a comprehensive multi-sensor recording of communal intake occasions for researchers interested in automatic detection of intake gestures.
Modern multi-sensors like the Move 4 provide researchers the ability to collect objectively a large dataset regarding the accelerometry and gyroscope-data. That is very important for automatic detection of intake gestures that is a key element of autonomic dietary monitoring. Read more and klick the article above.

Validating Accelerometers for the Assessment of Body Position and Sedentary Behavior

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There is growing evidence that sedentary behavior is a risk factor for mental health. Activity sensors play an important role in the investigation of sedentary behavior.
But how exactly do they measure sedentary activity, and where is the best place to measure it?
The following table gives a short overview of the validity of different activity sensors. Further information can be found in the article: Validating Accelerometers for the Assessment of Body Position and Sedentary Behavior.

Aktivitätssensoren und ihre Validität im Überblick

Parameter

Wearing place

Move 4

ActiGraph

ActiPal

Body Position
(sitting/lying)

thigh

hip

K=.97

K=.78

-

K=.67

K=.85

-

Sedentary Behaviour

thigh

hip

K=.95

K=.84

-

K=.69

K=.90

-

Giurgiu M. et al. (2019). Journal for the Measurement of Physical Behaviour.

Assessment of the circadian stimulus potential of an integrative lighting system in an office area

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Ambulatory Assessment and Mobile Monitoring are the expertise of movisens. In order to support scientific work in these areas, movisens has been supporting student projects over the last 10 years by providing free loans of sensors and software.
Once again a study idea has been successfully carried out by an innovative project!
Students of the University of Lund examined the influence of integrated lighting systems in the office in relation to well-being and health.
Read more in the publication: Assessment of the circadian stimulus potential of an integrative lighting system in an office area.

New White Paper Activity Algorithms

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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

New white paper available

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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.

White Paper Move II Validation Energy Expenditure

R-Peak Detection

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

Arousal

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.

White paper EdaMove Effects of Stimuli