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Physilog Sensors

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Activity Monitor

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Walking Analysis

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Running Analysis

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Shoulder testing

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Algo Library

  Physilog® motion sensor

Compact (11g) and versatile wireless inertial measurement unit (IMU) with barometer.

Toolkits & SDKs

Waterproof

Wireless

Detailed features

10D sensing capabilities

High-quality 3D accelerometer, 3D gyroscope, and a barometric pressure sensor,  with programmable ranges and sampling frequencies. In standalone recording or streaming mode.

Development Toolkit and SDKs,

Program, read signals, and check your sensor status with the free Research ToolKit Software on Mobile App and PC. (Or use dedicated MATLAB functions or APIs).

Optionnal GPS extension board

Order Physilog® sensors with built-in GPS.

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Synchronisation

Multiple Physilog® modules record synchronous signals. External third party lab systems can also be synchronized with the Physilog® synchro dongle on request.

Applications
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Inertial sensors data acquisition for new R&D projects

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Investigate optimal sensor configuration for your application

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Signal processing research project and teaching classes

gait-app_icons_blue-11  Activity Monitoring

Objective quantification and monitoring of daily activity using Physilog sensor.

Intuitive barcode

Auto calibrate

Scientifically validated

Detailed Features

Interactive Barcode Visualization

Integrates information about the type, intensity, and duration of body movements and activities

Automatic alignment and calibration

No need to care about the exact position of the sensor, our algorithm does it for you.

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Scientifically validated accuracy

Validated against gold standards in-lab on various populations including Older adult and Stroke Patients. Check out the publications.

Validation studies

Perruchoud, C., Buchser, E., Johanek, L.M., Aminian, K., Paraschiv‐Ionescu, A. and Taylor, R.S. “Assessment of physical activity of patients with chronic pain.” Neuromodulation: Technology at the Neural Interface 17, no. S1 (2014): 42-47.

Paraschiv-Ionescu, A., Buchser, E. and Aminian, K. “Unraveling dynamics of human physical activity patterns in chronic pain conditions.” Scientific reports 3 (2013): Article number 2019.

Paraschiv-Ionescu, A., Perruchoud, C., Buchser, E. and Aminian, K.   “Barcoding human physical activity to assess chronic pain conditions.” PloS one 7, no. 2 (2012): 32239.

Buchser, E., Paraschiv-Ionescu, A., Durrer, A. and Aminian, K. “Objective Measurement of Physical Activity in Patients with Chronic Lower Limb Pain Treated with Spinal Cord Stimulation.” Proceedings of the Medtronic Forum for Neuroscience and Neuro-Technology 2005, Springer Berlin Heidelberg (2007): 30-32.

Buchser, E., Paraschiv‐Ionescu, A., Durrer, A., Depierraz, B., Aminian, K., Najafi, B. and Rutschmann, B. “Improved physical activity in patients treated for chronic pain by spinal cord stimulation.” Neuromodulation: Technology at the Neural Interface 8, no. 1 (2005): 40-48.

Paraschiv-Ionescu, A., Buchser, E.E., Rutschmann, B., Codray, S. and Aminian, K. “Long-term monitoring of physical activity pattern in pain patients.” Proceedings of the Third IASTED International Conference on Biomedical Engineering (2005): 615.

Applications
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Posture and activities identification from long-term data

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Pre/post treatment evaluation in home environment

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Objective healthy status and sedentary behavior quantification

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Provide feedback to the patient

gait-app_icons_blue-12  Gait Analysis

Gait assessment of 25 spatio-temporal parameters in 1 minute with Physilog sensors.

Easy-to-use

Auto calibrate

peer-reviewed accuracy

Detailed Features

Easy-to-use, with mobile App, Mac and PC

One button to start the sensor, one click to get your report, with options to discard initiation and turns.

Automatic alignment and calibration

No need to care about the exact position of the sensor, our algorithm does it for you.

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25 outcome parameters, in pdf or xls

Gait spatio-temporal parameters including speed, variability, symmetry and toe clearance with color code for interpretation. Check out a sample report.

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Scientifically validated accuracy

Validated against gold standards in-lab on various populations including Older adults, Parkinson’s disease, Cerebral Palsy and Stroke. Check out the publications in the reference tab below.

Reference studies

K. Aminian, F. Dadashi, B. Mariani, C. Lenoble-Hoskovec and B. Santos-Eggimann et al. Gait Analysis Using Shoe-worn Inertial Sensors: How is foot clearance related to walking speed? The 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Seattle, Washington, USA, September 13-17, 2014.

F. Dadashi, B. Mariani, S. Rochat, C. Büla and B. Santos-Eggimann et al. Gait and Foot Clearance Parameters Obtained Using Shoe-Worn Inertial Sensors in a Large-Population Sample of Older Adults, in Sensors, vol. 14, num. 1, p. 443-457, 2014.

A. Brégou Bougeois, B. Mariani, K. Aminian, P.-y. Zambelli and C. Newman. Spatio-temporal gait analysis in children with cerebral palsy using foot-worn inertial sensors, in Gait and Posture, vol. 39, num. 1, p. 436-442, 2014.

B. Mariani, F. J. G. Vingerhoets, M. Castro Jiménez and K. Aminian. On-shoe wearable sensors for gait and turning assessment of patients with Parkinson’s disease, in IEEE Transactions on Biomedical Engineering Bme, vol. 60, num. 1155-158, 2013.

B. Mariani, H. Rouhani, X. Crevoisier and K. Aminian. Quantitative estimation of foot-flat and stance phase of gait using foot-worn inertial sensors, in Gait and Posture, vol. 37, num. 2, p. 229 – 234, 2013.

S. Rochat, B. Mariani, C. Lenoble-Hoskovec, F. Botrugno and C. Piot-Ziegler et al. The Relationship Between Fear Of Falling And Foot Clearance In Older PeopleGerontologist, vol. 52, p. 435-436. Oxford Univ Press Inc, 2012

B. Mariani, s. rochat, c. bula and K. Aminian. Heel and Toe Clearance Estimation for Gait Analysis Using Wireless Inertial Sensors, in IEEE Transactions on Biomedical Engineering Bme, vol. 59, num. 11, p. 3162-3168,2012.

Applications
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Pre/post treatment evaluation in routine

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Objective Fall risk & motor symptoms assessment in clinical protocols

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Provide feedback to the patient

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Teaching classes in biomechanics and physiotherapy

gait-app_icons_blue-13  Running Analysis

Fast and precise running measure in natural conditions using Physilog sensors. The only reliable analysis of foot running technique.

Instant results

Auto calibrate

take it everywhere

Detailed Features

Easy-to-use and get rapid results

One button to start the sensor, one click to get your report thanks to the intuitive report on the tablet.

Automatic alignment and calibration

No need to care about the exact position of the sensor, our algorithm does it for you.

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Foot strike parameters, in pdf or xls

Running strike spatio-temporal parameters including pronation angle, strike angle, contact time, and symmetry with color code for interpretation.

Take it everywhere

No need to ask your subject to run on a treadmill, let the runner be tested in natural conditions. Since the system fits in a portable transport wallet, you can even take your kit to the field and events.

Applications
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In-shop shoe fitting based on running technique

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Coaching follow-up to measure progresses

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Convenient tool for scientific research project and sports medicine

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Teaching classes in biomechanics and running science

gait-app_icons_blue-14  Shoulder testing

HandsUp mobile App provides validated shoulder angles and kinematic scores (B-B), by simply using smartphone sensors.

Free download

Auto calibrate

peer-reviewed accuracy

Detailed Features

Free download

Instrumented shoulder test App measures kinematic scores for shoulder function from simple movements of the arm using a smartphone. Hands Up App by Gait Up SA is available on Android App Store. 100 free analysis included, 10CHF fee for unlimited usage.

Automatic alignment and calibration

No need to care about the exact position of the smartphone on the arm, our algorithm does it for you.

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Scientifically validated accuracy

Hands Up (former iShould) is the result of a collaboration between Ecole Polytechnique Fédérale de Lausanne (EPFL), CHUV and HESAV. It has been extensively validated against gold standards clinical scores in the publications below. Medical professionals use Hands Up to perform rapid and easy functional shoulder tests, to objectively measure and compare shoulder function between the left and right sides.

Reference studies

Pichonnaz, C., Duc, C., Jaccard, H., Ancey, C., Lécureux, E., Aminian, K., Farron, A,  Jolles, B.M. and Gleeson, N. “Comparison of a dedicated body-worn inertial system and a smartphone for shoulder function and arm elevation evaluation.” Physiotherapy 101 (2015): e1205-e1206.

Pichonnaz, C., Lecureux, E., Bassin, J.P., Duc, C., Farron, A., Aminian, K., Jolles, B.M. and Gleeson, N.   “Enhancing clinically-relevant shoulder function assessment using only essential movements.” Physiological measurement 36, no. 3 (2015): 547.

Pichonnaz, C., Duc, C., Jaccard, H., Ancey, C., Lécureux, E., Aminian, K., Farron, A., Jolles, B.M. and Gleeson, N.   “Validity of a straightforward shoulder function evaluation method using a smartphone.” Physiotherapy 101 (2015): e1206.

Pichonnaz, C., Duc, C., Gleeson, N., Ancey, C., Jaccard, H., Lécureux, E., Farron, A., Jolles, B.M. and Aminian, K. “Measurement Properties of the Smartphone-Based BB Score in Current Shoulder Pathologies.” Sensors 15, no. 10 (2015): 26801-26817.

Pichonnaz, C., Duc, C., Jolles, B.M., Aminian, K., Bassin, J.P. and Farron, A.   “Alteration and recovery of arm usage in daily activities after rotator cuff surgery.” Journal of Shoulder and Elbow Surgery 24, no. 9 (2015): 1346-1352.

Duc, C., Pichonnaz, C., Bassin, J. P., Farron, A., Jolles, B. M., & Aminian, K. “Evaluation of muscular activity duration in shoulders with rotator cuff tears using inertial sensors and electromyography.” Physiological measurement 35, no. 12 (2014): 2389.

Duc, C., Farron, A., Pichonnaz, C., Jolles, B. M., Bassin, J. P., & Aminian, K. “Distribution of arm velocity and frequency of arm usage during daily activity: objective outcome evaluation after shoulder surgery.” Gait & posture 38, no. 2 (2013): 247-252.

Oïhénart, L., Duc, C., & Aminian, K. “iShould: Functional evaluation of the shoulder using a Smartphone.” Gait & Posture 36 (2012): S61-S62.

Duc, C., Pichonnaz, C., Bassin, J. P., Farron, A., & Aminian, K. “Arm velocity distribution during daily activity: objective outcome evaluation after shoulder surgery.” Proceedings of the XII International Symposium on 3D Analysis of Human Movement, no. EPFL-CONF-182413, pp. 12-14. 2012.

Duc, C., Pichonnaz, C., Bassin, J., Jolles, B., Djahangiri, A., Farron, A., & Aminian, K.   “New method for detection of shoulder movement during daily activity.” Gait & Posture 36 (2012): S2-S3.

Duc, C., Pichonnaz, C., Bassin, J., Jolles, B., Djahangiri, A., Farron, A., & Aminian, K. “Evaluation of shoulder mobility during daily activity using electromyography and kinematic data.” Gait & Posture 36 (2012): S63

Pichonnaz, C., Duc, C., Bassin, J.P., Sauvage Pasche, N., Djahangiri, A., Jolles-Haeberli, B., Aminian, K. and Farron, A. “Validation of a Smartphone application for shoulder elevation.” Swiss Medical Weekly, vol. 142, no. EPFL-CONF-182417, p. 37. Swiss Medical Publishers Ltd/EMH Swiss Medical Publishers, 2012.

Pichonnaz, C., Martin, E., Bassin, J.P., Duc, C., Farron, A., Jolles-Haeberli, B., Aminian, K. and Gleeson, N. “Development of a kinematic functional shoulder test including only essential movements.” Physiotherapy, vol. 97, no. EPFL-CONF-174393, p. 996. 2011.

gait-app_icons_blue-16  Algorithm Library

Best-in-class custom algorithm library in your product, to make sense of motion. Capture 15+ years of world leading scientific expertise through Gait Up’s partner program (OEM/Licensing). Reduce risks and time to market for your own application with motion sensors, in health, wellness, sports, entertainment, robotics or veterinary…

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Leading expertise