When Robots Offer Support: How Older Adults Experience Touch During Walking Guidance

Can humanoid robots guide older adults while walking in a way that feels both safe and comfortable?
And what kind of physical contact creates trust without causing additional stress?
The recent preprint study “Effects of Robotic Touch on Older Users During Walking Guidance by a Humanoid Robot” by Leonie Leven and colleagues from the Karlsruhe Institute of Technologie explores these questions.

Objectively Measuring Physiological Responses
Two movisens research sensors were used:
  • The EcgMove 4 recorded a single-channel ECG at a sampling rate of 1,024 Hz. The data were used to calculate heart rate and the heart rate variability parameters RMSSD and pNN50.
  • The EdaMove 4 captured electrodermal activity through two Ag/AgCl electrodes at a sampling rate of 32 Hz. The analysis included skin conductance level, the number of skin conductance responses, and their mean amplitude.

Gentle Physical Contact Receives a Positive Response
The ECG data indicated a slight tendency toward higher stress during the interaction with the robot: heart rate increased marginally, while the HRV parameters examined decreased slightly.
For electrodermal activity, the response amplitude was slightly higher in some of the contact-based conditions than during contactless guidance.
The subjective and behavioral findings were generally positive. Holding the robot and resting the forearm on it were particularly associated with greater perceived safety, trust, and comfort. The results therefore suggest that older adults may prefer gentle, stable touch during robot-assisted walking guidance to an entirely contactless interaction.

Multidimensional Research Under Realistic Conditions
The study demonstrates the value of a multimodal research approach. Questionnaires alone may reflect conscious evaluations and socially desirable responses. ECG and electrodermal activity complement these reports by capturing continuous and largely involuntary physiological reactions.
This is precisely where the movisens product portfolio offers a wide range of research possibilities. The EcgMove 4 combines ECG and physical activity monitoring in a mobile system. The EdaMove 4 combines electrodermal activity with movement and contextual information. These sensors allow researchers to collect raw data not only in the laboratory but also in ambulatory studies conducted in participants’ everyday lives. Via Bluetooth, selected live parameters can also be connected to the movisensXS experience sampling platform. This makes it possible, for example, to trigger questionnaires or interventions in response to physiological changes or activity-related events.
Building on this approach, future studies could investigate how the acceptance of assistive robots develops over longer periods and in real-world care or clinical settings. Combining objective biosignals, physical activity data, and situation-specific self-reports provides a particularly informative methodological foundation for this research.

robot offers support

Advancing Inclusive Research: Validated Activity Intensity Cut-Points for Young Manual Wheelchair Users

New Study validates the Activity Sensor Move 4 for Motion Analysis in Children and Young Adolescents Using Manual Wheelchairs
Objective movement measurement is a key prerequisite for evidence-based research. A recent study by Friedrich-Alexander University Erlangen-Nürnberg published in the Journal of NeuroEngineering and Rehabilitation marks an important milestone in this field. For the first time the movisens Move 4 - Activity Sensor has been validated and calibrated specifically for children and adolescents who use manual wheelchairs.

The aim of the study was to establish robust activity intensity thresholds ranging from sedentary behavior to vigorous wheelchair propulsion. Heart rate and energy expenditure were also measured as physiological reference parameters. The results demonstrate that the Move 4 accurately and reliably captures different levels of physical activity. In particular, the high measurement stability and the newly developed population-specific activity thresholds provide an important foundation for future research in inclusive physical activity and rehabilitation. These findings enable a more objective and precise assessment of physical activity and movement behavior in children and adolescents using manual wheelchairs.

Close collaboration between researchers and technology providers is a key factor in the success of projects like this. This collaboration enabled reliable data collection and laid the foundation for the successful validation of the system. We are proud that our technology contributes to innovative, practice-oriented research. Together with our scientific partners we are advancing objective movement analysis and helping to create new opportunities for research, rehabilitation, and evidence-based healthcare.


Child in Wheelchair

From Sensor Technology to Research Infrastructure

Today, researchers studying the relationship between physical activity, movement, and mood in everyday life increasingly rely on movisens as a central part of their work.

What began as a combination of scientifically validated wearable sensors and innovative EMA technology has grown into an established infrastructure for behavioral and health research. Research teams around the world use movisens to measure experiences and behaviors where they actually happen—in people's daily lives.
A recent meta-analysis published in Nature Human Behaviour highlights movisens' role in the field. The study brings together findings from a large number of investigations examining the relationship between movement and mood and represents one of the most comprehensive analyses conducted to date. Notably, researchers carried out many of the included studies using movisens technology.
This achievement reflects more than the widespread adoption of sensors and apps. Over the years, researchers have built a shared ecosystem around movisens—one that supports comparable data, reproducible methods, and the integration of findings across studies.
Today, movisens delivers far more than sensor technology or EMA software. It provides a research infrastructure that makes real-world behavior and lived experiences measurable. Researchers use this infrastructure to understand the dynamics of human behavior beyond the laboratory and to answer questions that are becoming increasingly important for prevention, mental health, and personalized interventions.

If researchers want to understand real life, they must measure people in real life. Every day, movisens enables researchers around the world to do exactly that.


circle of movisens

Do you know who won the movisens Student Project Award 2025? What sort of study is it, then?

Winner of the Student Project 2025 Announced

We are proud to announce the winner of the movisens Student Project 2025, an award that recognizes outstanding innovation, creativity, and real-world impact.
This year’s winning project, “Embodied Experience of Fear – An Empirical Study on Female-Specific Safety in Urban Landscapes Using the Example of the City of Nürnberg,” was created by Annkathrin Dilly from the Catholic University of Eichstätt-Ingolstadt. The project stood out for its technical excellence, sustainability, and strong social impact, impressing the jury with both its clear problem identification and its compelling vision and execution.
The Student Project 2025 competition showcased a diverse range of inspiring ideas, highlighting how students are combining technology, research and creative thinking to address real-world challenges. Winning the movisens Student Project Award reflects not only exceptional technical skills, but also a deep understanding of psychological and physiological aspects, paired with forward-thinking design.
We warmly congratulate Annkathrin Dilly and Prof. Dr. Bachinger on this well-deserved achievement and look forward to seeing how this important research continues to evolve.

Congratulations to all participants and mentors who made Student Project 2025 a remarkable showcase of student innovation.

Would you like to join the Student Project 2026?

Apply now


happy woman

The 5 Most Common Mistakes When Measuring HRV

And how you can avoid them!

Heart Rate Variability (HRV) is a crucial metric for understanding the physiological stress and the autonomic nervous system (ANS) responses, particularly the parasympathetic system. For researchers, getting HRV measurements right is vital to ensure high-quality and reliable data. However, several common mistakes compromise the accuracy of HRV analysis. Let’s look at these common pitfalls and how to avoid them!

1. Using Consumer Devices
It’s tempting to opt for consumer-grade devices, especially when working with tight budgets. But these devices often don’t meet the standards required for accurate HRV measurement inresearch. They’re typically designed for everyday users rather than professionals, and they don’t provide access to raw data—making it difficult to ensure data accuracy.

How to Avoid This Mistake: Stick with reputable, research-grade brands that specialize in providing raw data and meet the standards for scientific research. If you can’t access raw data or need to log into a portal to view results, you're likely dealing with a consumer-grade device. These devices aren’t suitable for high-quality, publishable research.



EcgMove 4 worn
2. Using PPG in Ambulatory Measurements
Photoplethysmography (PPG) sensors can be useful for short-term, stationary measurements in a controlled environment. However, when it comes to real-world, ambulatory measurements, PPG can be prone to inaccuracies. It struggles to provide precise beat detection, which is essential for accurate HRV calculations. Movement artefacts and noise are common in real-world conditions.

How to Avoid This Mistake: For more accurate HRV data, use an Electrocardiogram (ECG), which has a clear reference point (the R peak in the ECG signal) for detecting heartbeats. This method is far more reliable, especially in dynamic settings where a participant might be moving. Even research-grade PPG devices can struggle with accuracy in real-life situations, so ECG is generally a safer choice.

3. Using Devices That Only Record IBI or Have a Low Sampling Frequency
Not all ECG devices are equal! Some only record Inter-Beat Intervals (IBI), meaning you won’t have access to the raw signal data to check for artefacts. Additionally, devices that sample at low frequencies (under 1000Hz) compromise the accuracy and precision of HRV measurements.

How to Avoid This Mistake: Ensure that the ECG device you’re using has a sampling frequency over 1000Hz and captures more than just IBIs. This way, you’ll have access to high-resolution data that allows you to view artefacts and ensure the integrity of your HRV measurements. Low-quality data can lead to skewed results, which undermines the value of your research.

4. Not Having Access to Raw Data
Raw data is essential for ensuring accurate HRV measurements. Without it, you cannot identify artefacts, noise, or anomalies in your data that could distort your findings. Without access to the raw ECG signal, you’re flying blind when it comes to interpreting HRV.

How to Avoid This Mistake: Always ensure that your measurement devices provide access to raw data. If the device doesn’t allow you to view or download the raw signals, it’s not suitable for use in research. This step is crucial for identifying issues in the data that could impact the overall analysis and interpretation.

5. Using ECG in Isolation
Even with a high-quality ECG that records raw data at a high sampling rate, using it in isolation can lead to incomplete interpretations. HRV data, by itself, doesn’t provide the full context of what’s happening with the participant. Understanding how environmental or physical factors (such as movement, temperature, or barometric pressure) influence the data is essential for an accurate analysis.

How to Avoid This Mistake: Incorporate additional signals like accelerometer, gyrometric, barometric, and temperature data alongside the ECG. These contextual data points help researchers understand the circumstances under which the HRV measurements occurred, giving more insight into how the participant's body responded to different situations. Context is crucial for accurate interpretation of HRV results.

Click here to read more about the best ways to capture accurate HRV data

Validity and reliability of psychophysiological data from wearable devices

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

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)

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

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.