Publications Library

Featuring Shimmer platform

Publications Library

Shimmer products are used in a wide range of clinical trials and studies with many papers being published from varying specialities.

If you have a paper which you would like included in the following list, we would love for you to share your publication. Please include any attachements or links as relevant.

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Shimmer White Papers Shimmer White Papers

Patterson, M. (2017). Shimmer Gait Algorithm Update. Click Here To View

Patterson, M. (2017). Shimmer Step Count - Estimating step count from a trunk mounted interial sensor. Click Here To View

Patterson, M. (2017). Shimmer Trunk Waist Gait Variable Description. Click Here To View

Gill, G. (2020). The Importance of Capturing Raw Sensor Data for Clinical Trials. In Journal of Clinical Research Best Practices - MAGI (Vol. 16, No. 5, May 2020) Click Here To View

Gill, G. (2020). Shimmer3 EBio Sensor for COVID-19 Clinical Trials Click Here To View

Gait Analysis and/or Fall Risk Gait Analysis and/or Fall Risk

Abbate, S., Avvenuti, M., Bonatesta, F., Cola, G., Corsini, P., & Vecchio, A. (2012). A smartphone-based fall detection system. Pervasive and Mobile Computing, 8(6), 883-899. doi:10.1016/j.pmcj.2012.08.003

Cola, G., Avvenuti, M., & Vecchio, A. (2015). An Unsupervised Approach for Gait-based Authentication. In Proceedings of the 2015 IEEE International Conference on Body Sensor Networks (BSN).

Cola, G., Avvenuti, M., Vecchio, A., Yang, G.-Z., & Lo, B. (2015). An On-Node Processing Approach for Anomaly Detection in Gait. IEEE Sensors Journal, 15(11), 6640–6649. doi: 10.1109/JSEN.2015.2464774

Doheny, E. P., Walsh, C., Foran, T., Greene, B. R., Fan, C. W., Cunningham, C., & Kenny, R. A. (2013). Falls classification using tri-axial accelerometers during the five-times-sit-to-stand test. Gait & Posture, 38(4), 1021-5. doi: 10.1016/j.gaitpost.2013.05.013

Ma, Y., Fallahzadeh, R., & Ghasemzadeh, H. (2015). Toward Robust and Platform-Agnostic Gait Analysis. In Proceedings of the 2015 IEEE International Conference on Body Sensor Networks (BSN).

Parisi, F., Ferrari, G., Giuberti, M., Contin, L., Azzaro, C., Albani, G., & Cimolin, V. (2015). On the Correlation between UPDRS Scoring in the Leg Agility , Sit-to-Stand , and Gait Tasks for Parkinsonians. In Proceedings of the 2015 IEEE International Conference on Body Sensor Networks (BSN) (pp. 1-6)

Rampp, A., Barth, J., & Schulein, S. (2015). Inertial Sensor Based Stride Parameter Calculation from Gait Sequences in Geriatric Patients. IEEE Transactions on Biomedical Engineering, In press(4), 1-8. doi:10.1109/TBME.2014.2368211

Sheehan, K. J., Greene, B. R., Cunningham, C., Crosby, L., & Kenny, R. A. (2014). Early identification of declining balance in higher functioning older adults, an inertial sensor based method. Gait & Posture, 39(4), 1034-9. doi:10.1016/j.gaitpost.2014.01.003

Walshe, E. A., Patterson, M. R., Commins, S., & Roche, R. A. P. (2015). Dual-task and electrophysiological markers of executive cognitive processing in older adult gait and fall-risk. Frontiers in Human Neuroscience, 9(200). doi:10.3389/fnhum.2015.00200

Yuwono, M., Su, S. W., Guo, Y., Moulton, B. D., & Nguyen, H. T. (2014). Unsupervised nonparametric method for gait analysis using a waist-worn inertial sensor. Applied Soft Computing, 14, 72-80. doi:10.1016/j.asoc.2013.07.027

Javier Conte Alcaraz, Sanam Moghaddamnia, Jürgen Peissig. (2016). An Android-based application for digital gait performance analysis and rehabilitation. doi:10.1109/HealthCom.2015.7454582

Johnston W., O'Reilly M., Dolan K., Reid N., F. Coughlan G. and Caulfield B. (2016). Objective Classification of Dynamic Balance Using a Single Wearable Sensor. In Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support - Volume 1: icSPORTS, ISBN 978-989-758-205-9, pages 15-24 doi:10.5220/0006079400150024

Leite, P., Postolache, O.(2017). Gait Rehabilitation Monitor doi:

Felix Kluge, Heiko Gaßner, Julius Hannink, Cristian Pasluosta, Jochen Klucken & Björn M. Eskofier. (2017). Towards Mobile Gait Analysis: Concurrent Validity and Test-Retest Reliability of an Inertial Measurement System for the Assessment of Spatio-Temporal Gait Parameters. doi:10.3390/s17071522

Tim Svensson. (2017). Cloud based platform for real time Gait analysis . doi:

Siddhartha Khandelwal, & Nicholas Wickstrom. (2017). Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database. doi:

Shinji Hotta, Akihiro Inomata, Yuki Sasamoto, Shiho Washizawa, & Brian Caulfield. (2017). Unsupervised Gait Detection Using Biomechanical Restrictions. doi:10.1109/EMBC.2017.8037735

Ahsan Shahzad, Seunguk Ko, Samgyu Lee, Jeong-A Lee, & Kiseon Kim. (2017). Quantitative Assessment of Balance Impairment for Fall-Risk Estimation Using Wearable Triaxial Accelerometer. doi:10.1109/JSEN.2017.2749446

Carlotta Caramia, Ivan Bernabucci, Carmen D'Anna, Cristiano De Marchis, & Maurizo Schmid. (2017). Gait parameters are differently affected by concurrent smartphone-based activities with scaled levels of cognitive effort. doi:10.1371/journal.pone.0185825

Marco Avvenuti, Nicola Carbonaro, Mario Cimino, Guglielmo Cola, Alessandro Tognetti, & Gigliola Vaglini. (2017). Smart shoe-based evaluation of gait phase detection accuracy using body-worn accelerometers. doi:

Samer K Al Kork, Itta Gowthami, Xavier Savatier, Taha Beyrouthy, Joe Akl Korbane, & Sherif Roshdi. (2017). Biometric database for human gait recognition using wearable sensors and a smartphone. doi:10.1109/BIOSMART.2017.8095329

Uriel Martinez-ernandez, Imran Mahmood, & Abbas A. Dehghani-Sanij. (2017). Simultaneous Bayesian recognition of locomotion and gait phases with wearable sensors. doi:

Amine Ait Si Ali, Marek Siupik, Abbes Amira, Faycal Bensaali, & Pablo Casaseca-de-la-Higuera. (2014). HLS based hardware acceleration on the zynq SoC: A case study for fall detection system. doi:10.1109/AICCSA.2014.7073266

Amir Baghdadi, Lora A. Cavuoto, & John L. Crassidis. (2018). Hip and Trunk Kinematics Estimation in Gait through Kalman Filter using IMU Data at the Ankle. doi:10.1109/JSEN.2018.2817228

Thiago de Quadros, André Eugenio Lazzaretti & Fábio Kürt Schneider. (2018). A Movement Decomposition and Machine Learning-Based Fall Detection System Using Wrist Wearable Device. doi:10.1109/JSEN.2018.2829815

Ryan Stephen Mattfeld.(2018). Evaluation of Pedometer Performance Across Multiple Gait Types Using Video for Ground Truth. doi:

Felix Kluge, Julius Hannink, Christian Pasluosta, Jochen Klucken, Heiko Gabner, Kolja Gelse, Bjoern M. Eskofier & Sebastian Krinner.(2018). Pre-operative sensor-based gait parameters predict functional outcome after total knee arthroplasty. doi:10.1016/j.gaitpost.2018.08.026

Elizabeth Walshe, Richard A.P. Roche, Christina Ward, Matt Patterson, Desmond O'Neill, Ronan Collins & Seán Commins.(2019). Comparable walking gait performance during executive and non-executive cognitive dual-tasks in chronic stroke: A pilot study. doi:10.1016/j.gaitpost.2019.05.004

Elhocine Boutellaa, Oussama Kerdjidj & Khaldia Ghanem.(2019). Covariance matrix based fall detection from multiple wearable sensors. doi:10.1016/j.jbi.2019.103189

Ricardo Espinosa, Hiram Ponce, Sebastian Gutierrez, Lourdes Martinez-Villasenor, Jorge Brieva & Ernesto Moya-Albor.(2020). Application of Convolutional Neural Networks for Fall Detection Using Multiple Cameras. doi:

R. Jansi & R. Amutha.(2020). Detection of fall for the elderly in an indoor environment using a tri-axial accelerometer and Kinect depth data. doi:

Scott Pardoel, Gaurav Shalin, Julie Nantel, Edward Lemaire & Jonathan Kofman.(2020). Selection of Plantar-Pressure and Ankle-Acceleration Features for Freezing of Gait Detection in Parkinson's Disease using Minimum-Redundancy Maximum-Relevance. doi:10.1109/EMBC44109.2020.9176607

Sports Sports

Akçetin, P. I., Ergen, S. C., & Sezgin, T. M. (2012). HMM based inertial sensor system for coaching of rowing activity. In Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). doi:10.1109/SIU.2012.6204805

Mitchell, E., Ahmadi, A., Connor, N. E. O., Richter, C., Farrell, E., Kavanagh, J., & Moran, K. (2015). Automatically Detecting Asymmetric Running using Time and Frequency Domain Features. In Proceedings of the 2015 IEEE International Conference on Body Sensor Networks (BSN).

O'Reilly, M., Whelan, D., Chanialidis, C., Friel, N., & Delahunt, E. (2015). Evaluating Squat Performance with a Single Inertial Measurement Unit. In Proceedings of the 2015 IEEE International Conference on Body Sensor Networks (BSN).

Phillips, C. W. G., Forrester, A. I. J., Hudson, D. A., & Turnock, S. R. (2014). Comparison of Kinematic Acquisition Methods for Musculoskeletal Analysis of Underwater Flykick. Procedia Engineering, 72, 56-61. doi:10.1016/j.proeng.2014.06.012

Richer, R., Blank, P., Schuldhaus, D., & Eskofier, B. M. (2014). Real-time ECG and EMG analysis for biking using android-based mobile devices. In Proceedings - 11th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2014 (pp. 104-108). doi:10.1109/BSN.2014.20

van der Kruk, E. , Schwab, A.L., van der Helm, F. C. T. , Veeger, H. E. J. , (2016). Wireless instrumented klapskates for longtrack speed skating. doi:

E. van der Kruk, O. den Braver, A. L. Schwab, F. C. T. van der Helm & H. E. J. Veeger (2016). Getting the angles straight in speed skating: a validation study on an IMU filter design to measure the lean angle of the skate on the straights; doi:

O'Reilly, M. , Whelan, D. , Ward, T. , Delahunt,E. , Caulfield, B. (2017). Technology in S&C: Tracking Lower Limb Exercises with Wearable Sensors. doi:

Whelan, D. F. , O'Reilly, M. A. , Ward, T.E. , Delahunt, E. , Caulfield, B. (2016). Technology in Rehabilitation: Evaluating the Single Leg Squat Exercise with Wearable Inertial Measurement Units doi:

Logan Lucas, Benjamin England, Travis Mason, Christopher Lanning, Taylor Miller, Alexander Morgan, & Thomas Almonroeder (2018). Decision-Making Influences Tibial Impact Accelerations During Lateral Cutting. doi:10.1123/jab.2017-0397

André Paiva, André Catarino, Hélder Carvalho, Octavian Postolache, Gabriela Postalche & Fernando Ferreira (2018). Design of a Long Sleeve T-Shirt with ECG and EMG for Athletes and Rehabilitation Patients. doi:10.1007/978-3-319-91334-6_34

Anton Umek & Anton Kos.(2018). SMART Equipment Design Challenges for Real-time Feedback Support in Sports. doi:10.22190/FUME171121020U

Robyn F. Madden, Kelly Anne Erdman, Jane Shearer & Lawrence L. Spriet.(2019). Effects of Caffeine on Exertion, Skill Performance and Physicality in Ice Hockey. doi:10.1123/ijspp.2019-0130

Matevž Pustišek, Yu Wei, Yunchuan Sun, Anton Umek & Anton Kos.(2019). The role of technology for accelerated motor learning in sport. doi:

Activities of Daily Living Activities of Daily Living

Biswas, D., Cranny, A., Gupta, N., Maharatna, K., Achner, J., Klemke, J., ... Ortmann, S. (2014). Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification. Human Movement Science, 40C, 59-76. doi:10.1016/j.humov.2014.11.013

Cleland, I., Nugent, C., Finlay, D., & Armitage, R. (2010). Optimal placement of accelerometers within the constraints of a smart garment system. In Proceedings of the IEEE/EMBS Region 8 International Conference on Information Technology Applications in Biomedicine, ITAB. doi:10.1109/ITAB.2010.5687791

Fortune, E., Tierney, M., Scanaill, C. N., Bourke, A., Kennedy, N., & Nelson, J. (2011). Activity level classification algorithm using SHIMMER wearable sensors for individuals with rheumatoid arthritis. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp. 3059-3062). doi:10.1109/IEMBS.2011.6090836

Lee, S. I., Ozsecen, M. Y., Della Toffola, L., Daneault, J.-F., Puiatti, A., Patel, S., & Bonato, P. (2015). Activity Detection in Uncontrolled Free-living Conditions Using a Single Accelerometer. In Proceedings of the 2015 IEEE International Conference on Body Sensor Networks (BSN).

Amir Mehmood, Akhter Raza, Adnan Nadeem, Umair Saeed. (2016). Study of Multi-Classification of Advanced Daily Life Activities on SHIMMER Sensor Dataset. doi:NA - View Here

Moiz Ahmed, Nadeem Mehmood, Adnan Nadeem, Amir Mehmood, & Kashif Rizwan. (2017). Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data. doi:10.4258/hir.2017.23.3.147

Hongyu Chen, Xiao Gu, Zhenning Mei, Ke Xu, Kai Yan, Chunmei Lu, Laishuan Wang, Feng Shu, Qixin Xu, Sidarto Oetomo, & Wei Chen. (2017). A Wearable Sensor System for Neonatal Seizure Monitoring. doi:10.1109/BSN.2017.7935999

Gert Mertes, Hans Hallez, Bart Vanrumste, & Tom Croonenborghs. (2017). Detection of chewing motion in the elderly using a glasses mounted accelerometer in a real-life environment. doi:10.1109/EMBC.2017.8037861

Charlotte Thodberg Jensen, Anne Mette Karnoe Jessen, Lotte Ishoy Jorgensen, Jeanette Kolbaek Laursen, Lars Bo Larsen, & Jacob Lyng Wieland. (2017). Using Biometric Data to Assess Affective Response to Media Experiences. doi:10.1007/978-3-319-41661-8_45

Congcong Ma, Qimeng Li, Wenfeng Li, Raffaele Gravina, Yu Zhang, & Giancarlo Fortino. (2017). Activity Recognition of Wheelchair Users Based on Sequence Feature in Time-series. doi:

Peter R. Worsley, Dan Rebolledo, Sally Webb, Silvia Caggiari, & Dan L. Bader. (2017). Monitoring the biomechanical and physiological effects of postural changes during leisure chair sitting. doi:10.1016/j.jtv.2017.10.001

Nahed Jalloul, Fabienne Porée, Geoffrey Viardot, Phillip L'Hostis, & Guy Carrault. (2017). Activity Recognition Using Complex Network Analysis. doi:10.1109/JBHI.2017.2762404

Michael Georg Niestroj. (2017). Real-time PC application development for evaluating hands rotations using inertial sensors. doi:

Spiros Nikolopulos, Panagiotis C. Petrantonakis, Kostas Georgiadis, Fotis Kalaganis, Georgios Liaros Ioulietta Lazarou, Katerina Adam, Anastasios Papazoglou-Chalikias, Elisavet Chatzilari, Vangelis P. Oikonomou, Chandan Kumar, Raphael Menges Steffen Staab, Daniel Muller, Korok Sengupta, Devasti Bostantjopoulou, Zoe Katsarou, Gabi Zellig, Meir Plotnik Amihai Gotlieb, Racheli Kizoni, Sofia Fountoukidou, Japp Ham, Dimitios Athanasiou, Agnes Mariakaki Dario Comanducci, Edoardo Sabatini, Walter Nistico, Markus, & Ionnis Kompatsiaris. (2017). A multimodal dataset for authoring and editing multimedia content: The MAMEM project. doi:10.1016/j.dib.2017.10.072

Jogile Kuklyte, Leonardo Gualano, Ghanashyama Prabhu, Kaushik Venkatarman, Deirdre Walsh, Catherine Woods, Kieran Moran & Noel E. O'Connor. (2017). MedFit: a Mobile Application for Recovering CVD Patients. doi:

Joong Woo Ahn, Se Hee Hwang, Chiyul Yoon, Joonnyong Lee, Hee Chan Kim & Hyung-Jin Yoon. (2017). Unobtrusive Estimation of Cardiorespiratory Fitness with Daily Activity in Healthy Young Men. doi:10.3346/jkms.2017.32.12.1947

Seul-Kee Kim & Hang-Bong Kang. (2017). An analysis of fear of crime using multimodal measurement. doi:10.1016/j.bspc.2017.12.003

Angel Jimenez-Molina, Cristian Retamal, & Hernan Lira. (2017). Using Psychophysiological Sensors to Assess Mental Workload During Web Browsing. doi:10.3390/s18020458

Rui Neves Madeira, Andre Antunes, & Octavian Postolache. (2017). Web applications and web services support therapists in a multi-sensor platform for therapeutic gaming. doi:10.1145/3151759.3151839

Umberto Barcaro, Paolo Barsocchi, Antonio Crivello, Franca Delmastro, Flavio Di Martino, Emanuele Distefano, Cristina Dolciotti Davide La Rosa, Massimo Magrini, & Filippo Palumbo. (2017). INTESA: An integrated ICT solution for promoting wellbeing in older people. doi:

Muhammad Awais, Lorenzo Chiari, Espen Alexander F. Ihlen, Jorunn Helbostad, & Luca Palmerini. (2018). Physical Activity Classification for Elderly People in Free Living Conditions. doi:10.1109/JBHI.2018.2820179

Albert Clapés, Àlex Pardo, Oriol Pujol Vila & Sergio Escalera. (2018). Action detection fusing multiple Kinects and a WIMU: an application to in-home assistive technology for the elderly. doi:10.1007/s00138-018-0931-1

Shashank Shivarudrappa. (2018). Real-time physiological identification using incremental learning and semi-supervised learning. doi:

Sunghoon Lee, Catherine Adans-Dester, Matteo Grimaldi, Ariel Dowling, Peter Horak, Randie Black-Schaffer, Paolo Bonato & Joseph Gwin. (2018). Enabling Stroke Rehabilitation in Home andCommunity Settings: A Wearable Sensor-Based Approach for Upper-Limb Motor Training. doi:10.1109/JTEHM.2018.2829208

Dorin Moldovan, Marcel Antal, Claudia Pop, Adrian Olosutean, Tudor Cioara, Ionut Anghel & Ioan Salomie. (2018). Spark-Based Classification Algorithms for Daily Living Activities. doi:10.1007/978-3-319-91189-2_8

Vítor Viegas, J.M. Dias Pereira, Octavian Postolache & Pedro Silva Girão. (2018). Application of Force and Inertial Sensors to Monitor the Usage of Walker Assistive Devices. doi:10.3390/s18020540

Brady Brown, Daniel Park, Brenna Sheehan, Shaina Shikoff, Jeffrey Solomon, Jonathan Yang & Inki Kim. (2018). Assessment of human driver safety at Dilemma Zones with automated vehicles through a virtual reality environment. doi:10.1109/SIEDS.2018.8374733

Sandeep Reddy Bommu. (2019). PSO trained Artificial Neural Networks Methods for Estimating Human Energy Expenditure. doi:

Mina Nouredanesh, & James Tung. (2019). IMU, sEMG, or their cross-correlation and temporal similarities: Which features detects lateral compensatory balance reactions more accurately?. doi:10.1016/j.cmpb.2019.105003

Ben Nicholls, Chee Siang Ang, Kanjo Eiman, Panote Siriaraya, Woon-Hong Yeo & Anthanasios Tsanas. (2019). An EMG-based Eating Behaviour Monitoring System with Haptic Feedback to Promote Mindful Eating?. doi:

Angelica Poli, Susanna Spinsante, Chris Nugent & Ian Cleland. (2019). Improving the Collection and Understanding the Quality of Datasets for the Aim of Human Activity Recognition. doi:

Uwe Kockemann, Marjan Alirezaie, Jennifer Renoux, Nicolas Tsiftes, Mobyen Uddin Ahmed, Daniel Morberg, Maria Linden & Amy Loufti.(2020). Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes. doi:../Downloads/sensors-20-00879-v2.pdf

Veronika Szucs, Tibor Guzsvinecz & Attila Magyar.(2020). Movement Pattern Recognition in Physical Rehabilitation - Cognitive Motivation-based IT Method and Algorithms. doi:

Jesus D. Ceron, Christine F. Martindale, Diego M. Lopez, Felix Kluge & Bjoern M. Eskofier.(2020). Indoor Trajectory Reconstruction of Walking, Jogging, and Running Activities Based on a Foot-Mounted Inertial Pedestrian Dead-Reckoning System. doi:10.3390/s20030651

Surya Sharma.(2020). Detecting P Detecting Periods of Eating in E eriods of Eating in Everyday Life by Tracking W acking Wrist Motion — What is a Meal? . doi:

Inertial Signal Processing Inertial Signal Processing

Gietzelt, M., Schnabel, S., Wolf, K.-H., Büsching, F., Song, B., Rust, S., & Marschollek, M. (2012). A method to align the coordinate system of accelerometers to the axes of a human body: The depitch algorithm. Computer Methods and Programs in Biomedicine, 106(2), 97-103. doi:10.1016/j.cmpb.2011.10.014

Gietzelt, M., Wolf, K.-H., Marschollek, M., & Haux, R. (2013). Performance comparison of accelerometer calibration algorithms based on 3D-ellipsoid fitting methods. Computer Methods and Programs in Biomedicine, 111(1), 62-71. doi:10.1016/j.cmpb.2013.03.006

Shafigh, S., Zia, T., & Mouzehkesh, N. (2013). Wireless Accelerometer Sensor Data Filtering Using Recursive Least Squares Adaptive Filter. In 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (pp. 66-70). doi:10.1109/ISSNIP.2013.6529766

X. Li ,C. González Navas ,JL Garrido-Castro (2017). Reliability and validity of the measurement of cervical mobility in patients with axial spondyloarthritis using an inertial sensor. In Rehabilitación, Volume 51, Issue 1 , January-March 2017, 17-21. 66-70). doi:10.1016/j.rh.2016.10.002

Cagatay Catal, Akhan Akbulut (2018).Automatic Energy Expenditure Measurement for Health Science. doi:10.1016/j.cmpb.2018.01.015

Amine Ait Si Ali, Xiaojun Zhai, Abbes Amira, Faycal Bensaali, & Naeem Ramzan (2016). Enhanced Biometric Security and Privacy Using ECG on the Zynq SoC. doi:10.1007/978-3-319-47301-7_8

M. Ershad, R. Rege, & A. Majewicz Fey (2018). Meaningful Assessment of Robotic Surgical Style using the Wisdom of Crowds. doi:10.1007/s11548-018-1738-2

Tengku Nor Shuhada Tengku Zawawi, Abdul Rahim Abduallah, Rubita Sudriman, Norhashimah Mohd Saad, Jimgwei Too & Ezreen Farina Shair. (2019). Classification of EMG Signal for Health Screening Task for Musculoskeletal Disorder.

Oana Balan, Gabriela Moise, Alin Moldoveanu, Marus Leordeanu & Florica Moldoveanu.(2020). An Investigation of Various Machine and Deep Learning Techniques Applied in Automatic Fear Level Detection and Acrophobia Virtual Therapy. doi:10.3390/s20020496

Other Biophysical and Human Metrics Other Biophysical and Human Metrics

Ahamed, N. U., Sundaraj, K., Ahmad, R. B., Rahman, M., & Islam, M. A. (2012). Analysis of Right Arm Biceps Brachii Muscle Activity with Varying the Electrode Placement on Three Male Age Groups During Isometric Contractions Using a Wireless EMG Sensor. Procedia Engineering, 41, 61-67. doi:10.1016/j.proeng.2012.07.143

Billeci, L., Pioggia, G., Brunori, E., Crifaci, G., Tartarisco, G., Balocchi, R., ... Morales, M. A. (2012). Wearable sensors combined with wireless technologies for the evaluation of heart rate and heart rate variability in anorexia nervosa adolescents. Neuropsychiatrie de l'Enfance et de l'Adolescence, 60(5), S157. doi:10.1016/j.neurenf.2012.04.192

Cornelius, C., Peterson, R., Skinner, J., Halter, R., & Kotz, D. (2014). A wearable system that knows who wears it. In MobiSys '14 Proceedings of the 12th annual international conference on Mobile systems, applications, and services (pp. 55-67). doi:10.1145/2594368.2594369

Dehzangi, O., & Williams, C. (2015). Towards Multi-Modal Wearable Driver Monitoring: Impact of Road Condition on Driver Distraction. In Proceedings of the 2015 IEEE International Conference on Body Sensor Networks (BSN) (pp. 1-6).

Gradl, S., Leutheuser, H., Kugler, P., Biermann, T., Kreil, S., Kornhuber, J., ... Eskofier, B. (2013). Somnography using unobtrusive motion sensors and Android-based mobile phones. In 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1182-1185). doi:10.1109/EMBC.2013.6609717

Gutierrez Rivas, R., Dominguez, J. J. G., Marnane, W. P., Twomey, N., & Temko, A. (2013). Real-time allergy detection. In 2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013 - Proceedings. doi:10.1109/WISP.2013.6657476

Martinez-Manzanara, O., Roosma, E., Beudel, M., Borgemeester, R. W. K., van Laar, T., & Maurits, N. M. (2015). A method for automatic , objective and continuous scoring of bradykinesia. In Proceedings of the 2015 IEEE International Conference on Body Sensor Networks (BSN).

Pereira, O. R. E., Caldeira, J. M. L. P., Shu, L., & Rodrigues, J. J. P. C. (2014). An efficient and low cost Windows Mobile BSN monitoring system based on TinyOS. Telecommunication Systems, 55, 115-124. doi:10.1007/s11235-013-9756-4

Beirne, S., Diamond, D., Glennon, T., Matzeu, M., McCaul, M., O'Mahoney, N., O'Quigley, C., Stroiescu, F., Wallace, G., & White, P. (2016). "SWEATCH": A Wearable Platform for Harvesting and Analysing Sweat Sodium Content. doi:

Estrada, L., Torres, A., Sarlabous, L., & Jané R. (2016). Evaluating Respiratory Muscle Activity using a Wireless Sensor Platform. In Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the (pp. 5769-5772). IEEE. doi:10.1109/EMBC.2016.7592038

Tang, S., Yang, C., & Tavassolian, N. (2017). Utilizing Gyroscopes Towards the Automatic Annotation of Seismocardiograms. In IEEE Sensors Journal ( Volume: 17, Issue: 7, April 1, 1 2017 ) (pp. 2129-2136). IEEE. doi:10.1109/JSEN.2017.2663420

James Head, Matthew Tenan, Andrew Tweedell, Michael LaFiandra, Frank Morelli, Kyle Wilson, Samson Oretga, & William Helton (2017). Prior Mental Fatigue Impairs Marksmanship Decision Performance. doi:10.3389/fphys.2017.00680

Chelsea Dobbins & Stephen Fairclough (2017). A mobile lifelogging platform to measure anxiety and anger during real-life driving. doi:10.1109/PERCOMW.2017.7917583

Zahra Sedighi Maman, Mohammad Ali Alamdar Yazdi, Lora A. Cavuoto, & Fadel M. Megahed (2017). A data-driven approach to modeling physical fatigue in the workplace using wearable sensors. doi:10.1016/j.apergo.2017.02.001

Ahsan Shahzad, Seunguk Ko, Samgyu Lee, Jeong-A Lee, & Kiseon Kim (2017). Quantitative Assessment of Balance Impairment for Fall-Risk Estimation Using Wearable Triaxial Accelerometer. doi:10.1109/JSEN.2017.2749446

Azin Fathi, & Kevin Curran (2017). Detection of spine curvature using wireless sensors. doi:10.1016/j.jksus.2017.09.014

Chenxi Yang, & Negar Tavassolian (2017). Combined Seismo- and Gyro-cardiography: A More Comprehensive Evaluation of Heart-Induced Chest Vibrations. doi:10.1109/JBHI.2017.2764798

Gianluca Borghini, Martina Ragosta, Pietro Arico, Stefano Bonelli, Gianluca Di Flumeri, Nicolina Sciaraffa, Paola Tomasello David Mancini, Alfredo Colosimo, & Fabio Babiloni(2017). Development of Neurometrics for Selective Attention Evaluation in ATM. doi:

Tian Zhou, Jackie S. Cha, Glebys T. Gonzalez, Juan P. Wachs, Chandru Sundaram & Denny Yu(2018). Joint Surgeon Attributes Estimation in Robot-Assisted Surgery. doi:10.1145/3173386.3176981

Alexandria Remus, Brian Caulfield, Cailbhe Doherty, Colum Crowe, Giacomo Severini & Eamonn Delahunt(2018). A laboratory captured “giving way” episode in an individual with chronic ankle instability. doi:10.1016/j.jbiomech.2018.05.015

Maroun Koussaifi, Carol Habib & Abdallah Makhoul(2018). Real-time stress evaluation using wireless body sensor networks. doi:10.1109/WD.2018.8361691

Daniel Munguia Chang & Axel Hult(2018). Smartphone Acquisition and Online Visualization of IMU and EMG Sensor Data for Assessment of Wrist Load. doi:

Yanto Nadue(2018). The Effect of Real-time Biofeedback on Lumbar Spine and Lower Limb Kinematics and Kinetics during Repetitive Lifting. doi:

Donatas Luksys, Gintaras Jonaitis & Julius Griskevicius (2018). Quantitative Analysis of Parkinsonian Tremor in a Clinical Setting Using Inertial Measurement Units. doi:10.1155/2018/1683831

Francesca Fulceri, Alessandro Tonacci, Andrea Lucaferro, Fabio Apicella, Antonio Narzisi, Giulia Vincenti, Filippo Muratori & Annarita Contaldo.(2018). Interpersonal motor coordination during joint actions in children with and without autism spectrum disorder: The role of motor information. doi:10.1016/j.ridd.2018.05.018

Tim Op De Beeck, Wannes Meert, Kurt Schutte, Benedicte Vanwanseele & Jesse Davis.(2018). Fatigue Prediction in Outdoor Runners Via Machine Learning and Sensor Fusion. doi:10.1145/3219819.3219864

Ràfols-de-Urquía, Luis Estrada, Josep Estévez-Piorno, Leonardo Sarlabous, Raimon Jané, and Abel Torres.(2018). Evaluation of a wearable device to determine cardiorespiratory parameters from surface diaphragm electromyography. Published in: IEEE Journal of Biomedical and Health Informatics. doi:10.1109/JBHI.2018.2885138

Priyashri K. Sridhar, Samantha W.T. Chan, Yvonne Chua & Suranga Nanayakkara.(2019). Going beyond performance scores: Understanding cognitive-affective states in Kindergarteners and application of framework in classrooms. doi:10.1016/j.ijcci.2019.04.002

Paolo Pirovano, Akshay Shinde, Paddy White, Gordon Wallace, Margaret McCaul & Dermot Diamond.(2019). Real-time Analysis of Electrolytes in Sweat Through a Wearable Sensing Platform. doi:

Chenxi Yang, Clarel Antoine, Bruce K. Young & Negar Tavassolian.(2019). A Pilot Study on Fetal Heart Rate Extraction from Wearable Abdominal Inertial Sensors. doi:10.1109/JSEN.2019.2930886

Afiqah Sulaiman, Reem Musab, Azizan As'arry, Khairil Anas Md Rezali, Nawal Aswan Abdul Jalil, Raja Mohd Kamil Raja Ahmad & Mohd Zarhamdy Md Zain.(2019). Design, fabrication and analysis of tremor test rig to imitate human hand tremor. doi:

Pritam Sarkar, Kyle Ross, Aaron J. Ruberto, Dirk Rodenburg, Paul Hungler, & Ali Etemad.(2019). Classification of Cognitive Load and Expertise for Adaptive Simulation using Deep Multitask Learning. doi:


Jawad Hussain, Kenneth Sundaraj & Indra Devi Subramaniam.(2020). Cognitive stress changes the attributes of the three heads of the triceps brachii during muscle fatigue. doi:10.1371/journal.pone.0228089

Cristina Carmona-Pérez, Alberto Pérez-Ruiz, Juan L. Garrido-Castro, Francisco Torres Vidal, Sandra Alcaraz-Clariana, Lourdes García-Luque, Daiana Priscila Rodrigues-de-Souza & Francisco Alburquerque-Sendín.(2020). Design, Validity, and Reliability of a New Test, Based on an Inertial Measurement Unit System, for Measuring Cervical Posture and Motor Control in Children with Cerebral Palsy. doi:file:///C:/Users/ppadm/Downloads/diagnostics-10-00661.pdf

Joaquín Calatayud, Jonás Navarro-Navarro, Juan J. Carrasco, Sofía Pérez-Alenda, Carlos Cruz-Montecinos, Lars L. Andersen, Felipe Querol-Giner & José Casaña.(2020). EMG, Rate of Perceived Exertion, Pain, Tolerability and Possible Adverse Effects of a Knee Extensor Exercise with Progressive Elastic Resistance in Patients with Severe Haemophilia. doi:file:///C:/Users/ppadm/Downloads/jcm-09-02801.pdf

Sensor Networks - Communications, Platform and Power Considerations Sensor Networks - Communications, Platform and Power Considerations

Caldeira, J. M. L. P., Rodrigues, J. J. P. C., Lorenz, P., & Ullah, S. (2014). Impact of sensor nodes scaling and velocity on handover mechanisms for healthcare wireless sensor networks with mobility support. Computers in Industry. doi:10.1016/j.compind.2014.09.002

Chen, B., Varkey, J. P., Pompili, D., Li, J. K.-J., & Marsic, I. (2010). Patient vital signs monitoring using Wireless Body Area Networks. In Proceedings of the 2010 IEEE 36th Annual Northeast Bioengineering Conference (NEBEC) (pp. 1-2). doi:10.1109/NEBC.2010.5458139

Cornelius, C. T., & Kotz, D. F. (2012). Recognizing whether sensors are on the same body. Pervasive and Mobile Computing, 8(6), 822-836. doi:10.1016/j.pmcj.2012.06.005

Diallo, O., Rodrigues, J. J. P. C., Sene, M., & Niu, J. (2014). Real-time query processing optimization for cloud-based wireless body area networks. Information Sciences, 284, 84-94. doi:10.1016/j.ins.2014.03.081

Fernandez, F., & Fabero, J. C. (2011). An enhanced simulation tool for shimmer mote. In Proceedings of the 2011 Summer Computer Simulation Conference (pp. 44-51). Retrieved from

Fortino, G., Galzarano, S., Gravina, R., & Li, W. (2015). A framework for collaborative computing and multi-sensor data fusion in body sensor networks. Information Fusion, 22, 50-70. doi:10.1016/j.inffus.2014.03.005

Fortino, G., Parisi, D., Pirrone, V., & Di Fatta, G. (2014). BodyCloud: A SaaS approach for community Body Sensor Networks. Future Generation Computer Systems, 35, 62-79. doi:10.1016/j.future.2013.12.015

Mouzehkesh, N., Shafigh, S., Zia, T., & Zheng, L. (2013). Light-Weight History-Based Medium Access Control (MAC) Protocol for Body Area Networks. In 2013 Seventh International Conference on Sensing Technology (ICST) (pp. 91-96). doi:10.1109/ICSensT.2013.6727622

Sudha, G. F., Karthik, S., & Kumar, N. S. (2014). Activity aware energy efficient priority based multi patient monitoring adaptive system for body sensor networks. Technology and Health Care, 22(2), 167-177. doi:10.3233/THC-140782

Zapater, M., Arroba, P., Ayala, J. L., Moya, J. M., & Olcoz, K. (2014). A novel energy-driven computing paradigm for e-health scenarios. Future Generation Computer Systems, 34, 138-154. doi:10.1016/j.future.2013.12.012

Beny Nugraha, Irawan Ekasurya, Gunawan Osman, & Mudrik Alydrus. (2017). Analysis of Power Consumption Efficiency on Various IoT and Cloud-Based Wireless Health Monitoring Systems: A Survey . Modern Education and Computer Science Press. doi:10.5815/ijitcs.2017.05.05

Wenhan Liu, Mengxin, Yidan Zhang, Yuan Liao, Qijun Huang, Sheng Chang, Hao Wang, & Jin He. (2017). Real-time Multilead Convolutional Neural Network for Myocardial Infarction Detection. doi:10.1109/JBHI.2017.2771768

Dina Ganem Abunahia, Hala Raafat Abou Al Ola, Tasnim Ahmad Ismail, Abbes Amira, Amine Ait Si Ali & Faycal Bensaali. (2016). Generalised and Versatile Connected Health Solution on the Zynq SoC. doi:10.1007/978-3-319-69266-1_22

Xiaojun Zhai, Amine Ait Si Ali, Abbes Amira & Faycal Bensaali. (2016). ECG encryption and identification based security solution on the Zynq SoC for connected health systems. doi:10.1016/j.jpdc.2016.12.016

Valeria De Luca, Amir Muaremi, Oonagh Giggins, Lorcan Walsh & Ieuan Clay. (2018). Towards fully instrumented and automated assessment of motor function tests. doi:10.1109/BHI.2018.8333375

Tian Zhou, Jackie S. Cha, Glebys Gonzalez, Juan P. Wachs, Chandru P. Sundaram & Denny Yu. (2020). Multimodal Physiological Signals for Workload Prediction in Robot-assisted Surgery. doi:10.1145/3368589

Martin Lochner, Andreas Duenser & Shouvojit Sarker. (2020). Trust and Cognitive Load in semi-automated UAV operation. doi:10.1145/3369457.3369509

Vehicular and Environmental Monitoring Vehicular and Environmental Monitoring

Ahsan, M., Mcmanis, J., & Hashmi, M. S. J. (2014). Prototype System Development for Wireless Vehicle Speed Monitoring. In 9th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP) (pp. 287-292). doi:10.1109/CSNDSP.2014.6923841

Bennett, S. S., Brooks, C. J., Winden, B., Taunton, D. J., Forrester, A. I. J., Turnock, S. R., & Hudson, D. A. (2014). Measurement of ship hydroelastic response using multiple wireless sensor nodes. Ocean Engineering, 79, 67-80. doi:10.1016/j.oceaneng.2013.12.011

O'Connell, E., Healy, M., O'Keeffe, S., Newe, T., & Lewis, E. (2013). A mote interface for fiber optic spectral sensing with real-time monitoring of the marine environment. IEEE Sensors Journal, 13(7), 2619-2625. doi:10.1109/JSEN.2013.2258760

Rajesh Singh, Anita Gehlot, Vijay Singh, Vikas Garg, Suresh Kumar, Sushabhan Choudhury, & Rupendra Pachauri. (2017). Role of Automation in Construction Industries. Journal of Engineering Technology, 6(2), 799-831. doi:

Lis P. Tussyadiah, & Sangwon Park. (2017). Consumer Evaluation of Hotel Service Robots. doi:10.1007/978-3-319-72923-7_24

Uriel Martinez-Hernandez, & Abbas A. Dehghani-Sanij. (2018). Probabilistic identification of sit-to-stand and stand-to-sit with a wearable sensor. doi:10.1016/j.patrec.2018.03.020

Claudia Krogmeier, Christos Mousas, & David Whittinghill. (2019). Human, Virtual Human, Bump! A Preliminary Study on Haptic Feedback.

Nigel Bosch, & Sidney D'Mello. (2019). Automatic Detection of Mind Wandering from Video in the Lab and in the Classroom. doi:10.1109/TAFFC.2019.2908837

Andreas Simskar Wulvik, Martin Steinart & Henrikke Dybvik. (2019). Investigating the relationship between mental state (workload and affect) and physiology in a control room setting (ship bridge simulator). doi:10.1007/s10111-019-00553-8

J. Wang, JM Warnecke & TM Deserno. (2019). The Vehicle as a Diagnostic Space: Efficient Placement of Accelerometers for Respiration Monitoring During Driving. doi:

Claudia Krogmeier & Christos Mousas. (2020). Eye fixations and electrodermal activity during low-budget virtual reality embodiment. doi:

Yue Hu, Mehdi Benallegue, Gentiane Venture & Eiichi Yoshida. (2020). Interact With Me: An Exploratory Study on Interaction Factors for Active Physical Human-Robot Interaction. doi:10.1109/LRA.2020.3017475

Manuel Seet, Rafiul Amin, Nida Abbasi, Junji Hamano, Anumita Chaudhury, Anastasios Bezerianos, Rose Faghih, Nitish Thakor & Andrei Dragomir. (2020). Olfactory-induced Positive Affect and Autonomic Response as a Function of Hedonic and Intensity Attributes of Fragrances. doi:10.1109/EMBC44109.2020.9176095

Ginaluca Borghini, Andrea Bandini, Silvia Orlandi, Gianluca Flumeri, Pietro Arico, Nicolina Sciaraffa & Vincenzo Ronca. (2020). Stress Assessment by Combining Neurophysiological Signals and Radio Communications of Air Traffic Controllers. doi:10.1109/EMBC44109.2020.9175958

Na Du, Feng Zhou, Elizabeth Pulver, Dawn Tilbury, Lionel Robert, Anuj Pradhan & Jessie Yang. (2020). Predicting Driver Takeover Performance in Conditionally Automated Driving. doi:

Neuroscience Neuroscience

Caviedes, A, & Figliozzi, M. (2018). Modeling the impact of traffic conditions and bicycle facilities on cyclists’ on-road stress levels. Doi

McKenna K. Tornblad, Luke Lapresi, Christopher M. Homan, Raymond W. Ptucha & Cecilia Ovesdotter Alm. (2018). "Sensing and Learning Human Annotators Engaged in Narrative Sensemaking"

B. Brown et al., "Assessment of human driver safety at Dilemma Zones with automated vehicles through a virtual reality environment," 2018 Systems and Information Engineering Design Symposium (SIEDS), Charlottesville, VA, 2018, pp. 185-190. doi: 10.1109/SIEDS.2018.8374733

M. Koussaifi, C. Habib and A. Makhoul, "Real-time stress evaluation using wireless body sensor networks," 2018 Wireless Days (WD), Dubai, 2018, pp. 37-39. doi: 10.1109/WD.2018.8361691

KUMAR AKASH, WAN-LIN HU, NEERA JAIN, and TAHIRA REID, "A Classification Model for Sensing Human Trust in Machines Using EEG and GSR", 2018. doi:

Ying-Feng Kuo and Li-Te Liu, "The Effects of Framing and Cause-Related Marketing on Crowdfunding Sponsors' Intentions: A model Development", 2014. doi: 10.1145/2684103.2684170

Jainendra Shukla, Miguel Barreda-Ángeles, Joan Oliver and Domènec Puig, "Efficient wavelet-based artifact removal for electrodermal activity in real-world applications", Biomedical Signal Processing and Control, volume 42, 2018, pp. 45-52. doi:

Nikolopoulos, S et al., "A multimodal dataset for authoring and editing multimedia content", 2017. doi: 10.1016/j.dib.2017.10.072

Seul-Kee Kim and Hang-Bong Kang, "An analysis of fear of crime using multimodal measurement", Biomedical Signal Processing and Control, volume 41, 2018, pp. 186-197. doi:

Ubaldo Custa, Luz Martinez-Martinez and Jose Ignacio Nino, "A Case - Study in Neuromarketing: Analysis of the Influence of Music on Advertising Effectivenes through Eye-Tracking, Facial Emotion and GSR", European Journal of Social Science Education Research, volume 5, 2018, pp. 84-93. doi: