Examples of academic publications using Movesense:


ECG, heart rate and heart rate variability research

Atrial fibrillation

  • Automatic Mobile Health Arrhythmia Monitoring for the Detection of Atrial Fibrillation: Prospective Feasibility, Accuracy, and User Experience Study, Onni E Santala, Jari Halonen, Susanna Martikainen, Helena Jäntti, Tuomas T Rissanen, Mika P Tarvainen, Tomi P Laitinen, Tiina M Laitinen, Eemu-Samuli Väliaho, Juha E K Hartikainen, Tero J Martikainen, Jukka A Lipponen, Link:
  • Effectiveness of the Chest Strap Electrocardiogram to Detect Atrial Fibrillation, Hartikainen S, Lipponen JA, Hiltunen P, Rissanen TT, Kolk I, Tarvainen MP, Martikainen TJ, Castren M, Väliaho ES, Jäntti H., Am J Cardiol. 2019 May 15;123(10):1, 643-1648. doi: 10.1016/j.amjcard.2019.02.028. Epub 2019 Feb 23, Link:
  • Commercially-available heart rate monitor repurposed into a 9-gram standalone device for automatic arrhythmia detection with snapshot electrocardiographic capability: a pilot validation, Chiara Martini, Bernardo Di Maria, Claudio Reverberi, Domenico Tuttolomondo, Nicola Gaibazzi, Link:

Heart rate variability

  • Estimation of Respiratory Frequency in Women and Men by Kubios HRV Software Using the Polar H10 or Movesense Medical ECG Sensor during an Exercise Ramp, Bruce Rogers, Marcelle Schaffarczyk, Thomas Gronwald, Link:
  • The Movesense Medical Sensor Chest Belt Device as Single Channel ECG for RR Interval Detection and HRV Analysis during Resting State and Incremental Exercise: A Cross-Sectional Validation Study, Bruce Rogers, Marcelle Schaffarczyk, Martina Clauß, Laurent Mourot, Thomas Gronwald, Link:
  • Comparison of Heart Rate Monitoring Accuracy between Chest Strap and Vest during Physical Training and Implications on Training Decisions, Jakub Parak, Mikko Salonen, Tero Myllymäki, Ilkka Korhonen, Link:
  • Fractal Correlation Properties of Heart Rate Variability: A New Biomarker for Intensity Distribution in Endurance Exercise and Training Prescription?, Thomas Gronwald, Bruce Rogers and Olaf Hoos, Link:

Sleep monitoring

  • An automated heart rate-based algorithm for sleep stage classification: Validation using conventional polysomnography and an innovative wearable electrocardiogram device
    Nicolò Pini, Ju Lynn Ong, Gizem Yilmaz, Nicholas I. Y. N. Chee, Zhao Siting, Animesh Awasthi, Siddharth Biju, Kishan Kishan, Amiya Patanaik, William P. Fifer, Maristella Lucchini, Link:
  • Instantaneous Heart Rate based sleep staging using deep learning models as a convenient alternative to Polysomnography, Y Jie Chen, Z Siting, K Kishan, A Patanaik, Link: ERJ Open Research 2021; 7: Suppl. 7, 63
  • Monitoring Newborn and Infant Sleep Respiration and Heart Rate with a Wearable Sensor, Natalia Acosta Leinonen, Master’s thesis, University of Helsinki, Faculty of Biological and Environmental Sciences, Link:


Movement related research


  • Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants, Manu Airaksinen, Anastasia Gallen, Anna Kivi, Pavithra Vijayakrishnan, Taru Häyrinen, Elina Ilén, Okko Räsänen, Leena M. Haataja, Sampsa Vanhatalo, Link:
  • Automatic Posture and Movement Tracking of Infants with Wearable Movement Sensors, Manu Airaksinen, Okko Räsänen, Elina Ilén, Taru Häyrinen, Anna Kivi, Viviana Marchi, Anastasia Gallen, Sonja Blom, Anni Varhe, Nico Kaartinen, Leena Haataja, Sampsa Vanhatalo, Link:
  • Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study, Jauhiainen M, Puustinen J, Mehrang S, Ruokolainen J, Holm A, Vehkaoja A, Nieminen H, Link: JMIR Res Protoc 2019;8(3):e12808

Health & Wellbeing

  • Mobile Fall Risk Assessment Solution for Daily-Life Settings, H. Similä, M. Immonen and T. Niemirepo, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018, pp. 1530-1533, Link: doi: 10.1109/EMBC.2018.8512589
  • Personalised eHealth intervention to increase physical activity and reduce sedentary behaviour in rehabilitation after cardiac operations: study protocol for the PACO randomised controlled trial, Ville Vasankari, Jari Halonen, Pauliina Husu, Henri Vähä-Ypyä, Kari Tokola, Jaana Suni, Harri Sievänen, Vesa Anttila, Juhani Airaksinen, Tommi Vasankari, Juha Hartikainen, Link:
  • Effects of reduced sedentary time on cardiometabolic health in adults with metabolic syndrome: A three-month randomized controlled trial, Taru Garthwaite, Tanja Sjöros, Saara Laine, Henri Vähä-Ypyä, Eliisa Löyttyniemi, Harri Sievänen, Noora Houttu, Kirsi Laitinen, Kari Kalliokoski, Tommi Vasankari, Juhani Knuuti, Ilkka Heinonen, Link:
  • An openly available wearable, a diaper cover, monitors infant’s respiration and position during rest and sleep, Jukka Ranta, Elina Ilén, Kirsi Palmu, Jonna Salama, Oleksii Roienko, Sampsa Vanhatalo, Link:
  • Evaluation of a New Simplified Inertial Sensor Method against Electrogoniometer for Measuring Wrist Motion in Occupational Studies, Karnica Manivasagam, Liyun Yang, Link:


  • Development and evaluation of an inertial measurement unit (IMU) system for jump detection and jump height estimation in beach volleyball, Samuel Schleitzer, Svenja Wirtz, Ross Julian, Eric Eils, Link:
  • Connected Skiing: Motion Quality Quantification in Alpine Skiing, Cory Snyde, Aaron Martínez, Rüdiger Jahnel,Jason Roe, Thomas Stöggl, Link:
  • A Single Sacral-Mounted Inertial Measurement Unit to Estimate Peak Vertical Ground Reaction Force, Contact Time, and Flight Time in Running, Aurélien Patoz, Thibault Lussiana, Bastiaan Breine, Cyrille Gindre, Davide Malatesta, Link:
  • Estimating running kinematics variability with an IMU sensor placed on the runner’s thorax, Andrea Zignoli, Damiano Fruet, Laurent Mourot, Link:
  • Climbing Activity Recognition and Measurement with Sensor Data Analysis, Iustina Ivanova, Marina Andric, Andrea Janes, Francesco Ricci, Floriano Zini, International Conference on Multimodal Interaction (ICMI ’20 Companion). Association for Computing Machinery, New York, NY, USA, 245–249., Link: DOI:

Activity classification, gesture recognition

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