Featuring Shimmer platform


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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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

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:

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

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

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

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

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: