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

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

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

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

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

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