Low-Complexity Design and Validation of Wireless Motion Sensor Node to Support Physiotherapy
2. Low Complexity Design of Wireless Motion Sensor Node
- Accuracy. The sensor node needs to be able to measure the human body movement with high precision. With proper calibration, it is possible to achieve a target accuracy of with a sampling frequency of 50 Hz .
- User-friendly. The device needs to be easy to use, capable of being operated by anyone, regardless of any medical or technical background. We opted to implement wireless charging to increase user-friendliness in operation and maintenance. The data is also wirelessly transferred to eliminate a mess of cables and thus providing freedom of movement.
- Autonomy. Users want to focus on the application rather than constantly thinking about charging the device. Therefore, an autonomy of at least 5 h and a charge time of less than 1.5 h is necessary.
- Affordable. To provide an appealing multi-purpose product for a wide range of applications, it needs to come at a low cost. That way, we want to reach a wide audience, both professionals as individuals.
2.3. Wireless Connectivity
2.4. Wireless Charging
2.5. Optimization for Low Energy
3. Validation with Easily Accessible Equipment
4. Validation with Real-Life Exercises
5. Opportunities in e-Treatment Applications and Extended Functionalities
5.1. Opportunities in Supporting e-Treatment in Physiotherapy
- The patient can perform the session more or less independently.
- The patient is abroad and wants to continue the treatment with the same physiotherapist. For example, elite athletes who have to travel a lot.
- A patient is not allowed to leave the house. The COVID-19 pandemic proved this to be a realistic scenario.
5.2. Extension to Multiple Sensor Nodes
6. Conclusions and Future Work
Conflicts of Interest
|BLE||Bluetooth Low Energy|
|DMP||Digital Motion Processor|
|DoF||Degrees of Freedom|
|FIFO||First In First Out|
|IMU||Inertial Measurement Unit|
|IoT||Internet of Things|
|NTC||Negative Temperature Coefficient|
|RTC||Real Time Counter|
|WBAN||Wireless Body Area Networks|
|WOM||Wake On Motion|
|WPC||Wireless Power Consortium|
|WPT||Wireless Power Transfer|
- Porciuncula, F.; Roto, A.V.; Kumar, D.; Davis, I.; Roy, S.; Walsh, C.J.; Awad, L.N. Wearable Movement Sensors for Rehabilitation: A Focused Review of Technological and Clinical Advances. PM&R 2018, 10, S220–S232. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Inertial Motion Capture System|Shop|Eliko. Available online: https://www.eliko.ee/shop/inertial-motion-capture-system/ (accessed on 27 October 2020).
- IMU Sensor Development Kit|Wireless IMU Sensor|9DOF Motion Sensor. Available online: https://www.shimmersensing.com/products/shimmer3-development-kit (accessed on 27 October 2020).
- Cappelle, J. Wireless Motion Sensor Node. Available online: https://github.com/DRAMCO/NOMADe-Wireless-Motion-Sensor-Node (accessed on 4 November 2020). [CrossRef]
- Brodie, M.; Walmsley, A.; Page, W. The static accuracy and calibration of inertial measurement units for 3D orientation. Comput. Methods Biomech. Biomed. Eng. 2008, 11, 641–648. [Google Scholar] [CrossRef] [PubMed]
- Vicon|Award Winning Motion Capture Systems. Available online: https://www.vicon.com/ (accessed on 9 September 2020).
- Kadir, K.; Yusof, Z.M.; Rasin, M.Z.M.; Billah, M.M.; Salikin, Q. Wireless IMU: A Wearable Smart Sensor for Disability Rehabilitation Training. In Proceedings of the 2018 2nd International Conference on Smart Sensors and Application (ICSSA), Kuching, Malaysia, 24–26 July 2018; pp. 53–57. [Google Scholar]
- Petropoulos, A.; Sikeridis, D.; Antonakopoulos, T. Wearable Smart Health Advisors: An IMU-Enabled Posture Monitor. IEEE Consum. Electron. Mag. 2020, 9, 20–27. [Google Scholar] [CrossRef]
- ICM-20948—TDK. Available online: https://www.invensense.com/products/motion-tracking/9-axis/icm-20948/ (accessed on 26 October 2019).
- Fei, Y.; Song, Y.; Xu, L.; Sun, G. Micro-IMU based Wireless Body Sensor Network. In Proceedings of the 33rd Chinese Control Conference, Nanjing, China, 28–30 July 2014; pp. 428–432. [Google Scholar]
- Madgwick, S.O.H.; Harrison, A.J.L.; Vaidyanathan, R. Estimation of IMU and MARG orientation using a gradient descent algorithm. In Proceedings of the 2011 IEEE International Conference on Rehabilitation Robotics, Zurich, Switzerland, 29 June–1 July 2011; pp. 1–7. [Google Scholar]
- EFM32 Happy Gecko Family EFM32HG Data Sheet. Available online: https://www.silabs.com/documents/public/data-sheets/efm32hg-datasheet.pdf (accessed on 6 November 2020).
- Understanding Euler Angles—CH Robotics. Available online: http://www.chrobotics.com/library/understanding-euler-angles (accessed on 6 May 2020).
- Tuupola, M. How to Calibrate a Magnetometer? Available online: https://appelsiini.net/2018/calibrate-magnetometer/ (accessed on 10 April 2020).
- Ergen, S. ZigBee/IEEE 802.15.4 Summary. UC Berkeley Sept. 2004, 10, 11. [Google Scholar]
- Bin Ab Rahman, A. Comparison of Internet of Things ( IoT ) Data Link Protocols. Available online: https://www.semanticscholar.org/paper/Comparison-of-Internet-of-Things-(-IoT-)-Data-Link-Rahman/1cf94e2ebb27aaecdae3742e444ca9e87314216b (accessed on 7 November 2020).
- Danbatta, S.J.; Varol, A. Comparison of Zigbee, Z-Wave, Wi-Fi, and Bluetooth Wireless Technologies Used in Home Automation. In Proceedings of the 2019 7th International Symposium on Digital Forensics and Security (ISDFS), Barcelos, Portugal, 10–12 June 2019; pp. 1–5. [Google Scholar]
- Proteus-II—Bluetooth Smart 5.0 Module (AMB2623). Available online: https://katalog.we-online.de/en/wco/WIRL_BTLE_5 (accessed on 26 October 2019).
- Cx51 User’s Guide: Floating-Point Numbers. Available online: http://www.keil.com/support/man/docs/c51/c51_ap_floatingpt.htm (accessed on 18 March 2020).
- NUCLEO-L4R5ZI—STM32 Nucleo-144 Development Board with STM32L4R5ZI MCU— STMicroelectronics. Available online: https://www.st.com/en/evaluation-tools/nucleo-l4r5zi.html (accessed on 17 March 2020).
- UART Receive Buffering—Simply Embedded. Available online: http://www.simplyembedded.org/tutorials/interrupt-free-ring-buffer/ (accessed on 17 March 2020).
- Semtech Releases Next-Generation LinkCharge® LP (Low Power) Wireless Charging Platform. Available online: https://www.semtech.com/company/press/semtech-releases-next-generation-linkcharge-lp-low-power-wireless-charging-platform (accessed on 6 November 2020).
- BQ5105xB High-Efficiency Qi v1.2-Compliant Wireless Power Receiver and Battery Charger. Available online: ti.com/lit/ds/symlink/bq51051b.pdf?ts=1604672421028 (accessed on 6 November 2020).
- Galaxy S10 Reverse Wireless Charging Feature: How to Use It. Available online: https://www.valuewalk.com/2019/03/galaxy-s10-reverse-wireless-charging/ (accessed on 6 November 2020).
- WE-WPCC Wireless Power Charging Receiver Coil 760308101214. Available online: https://www.we-online.com/catalog/datasheet/760308101214.pdf (accessed on 6 November 2020).
- Puers, R. Inductive Powering: Basic Theory and Application to Biomedical Systems, 1st ed.; Springer: Dordrecht, The Netherlands, 2009. [Google Scholar]
- Computer Controlled Pan-Tilt Unit Model PTU-D46. Available online: www.imagelabs.com/wp-content/uploads/2011/01/Specs-PTU-D46.pdf (accessed on 6 November 2020).
- Madgwick, S.O.H. An Efficient Orientation Filter for Inertial and Inertial/Magnetic Sensor Arrays; Internal report by x-io Technologies Limited: Bristol, UK, 30 April 2010. [Google Scholar]
- Shi, J.; Tomasi, C. Good Features to Track. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 21–23 June 1994; pp. 593–600. [Google Scholar]
- Isard, M.; Blake, A. CONDENSATION—Conditional Density Propagation for Visual Tracking. Int. J. Comput. Vis. 1998, 29, 5–28. [Google Scholar] [CrossRef]
- de Kok, J.; Vroonhof, P.; Snijders, J.; Roullis, G.; Clarke, M.; Peereboom, K.; van Dorst, P.; Isusi, I. Work-Related Musculoskeletal Disorders: Prevalence, Costs and Demographics in the EU; European Agency for Safety and Health at Work: Bilbao, Spain, 2019. [Google Scholar]
- Dierick, F.; Buisseret, F.; Brismée, J.M.; Fourré, A.; Hage, R.; Leteneur, S.; Monteyne, L.; Thevenon, A.; Thiry, P.; Van der Perre, L.; et al. Opinion on the Effectiveness of Physiotherapy Management of Neuro-Musculo-Skeletal Disorders by Telerehabilitation. Available online: https://www.ifompt.org/site/ifompt/Telerehab_EN.pdf (accessed on 27 October 2020).
- Coviello, G.; Avitabile, G.; Florio, A. A Synchronized Multi-Unit Wireless Platform for Long-Term Activity Monitoring. Electronics 2020, 9, 1118. [Google Scholar] [CrossRef]
- Claesson, E.; Marklund, S. Calibration of IMUs using Neural Networks and Adaptive Techniques Targeting a Self-Calibrated IMU. Master’s Thesis, Chalmers University of Technology, Gothenburg, Sweden, 2019. [Google Scholar]
|Power consumption (max)||100 mW||1 mW||100 mW||10 mW||>100 mW|
|Range (max)||100 m||30 m||100 m||<100 m||1000 m|
|Data rate (max)||250 kbps||100 kbps||2 Mbps||1 Mbps||54 Mbps|
|Price||Low||High||Very low||Very low||Average|
|Target Angle [°]||Reference [°]||Sensor [°]||Error [°]|
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Cappelle, J.; Monteyne, L.; Van Mulders, J.; Goossens, S.; Vergauwen, M.; Van der Perre, L. Low-Complexity Design and Validation of Wireless Motion Sensor Node to Support Physiotherapy. Sensors 2020, 20, 6362. https://doi.org/10.3390/s20216362
Cappelle J, Monteyne L, Van Mulders J, Goossens S, Vergauwen M, Van der Perre L. Low-Complexity Design and Validation of Wireless Motion Sensor Node to Support Physiotherapy. Sensors. 2020; 20(21):6362. https://doi.org/10.3390/s20216362Chicago/Turabian Style
Cappelle, Jona, Laura Monteyne, Jarne Van Mulders, Sarah Goossens, Maarten Vergauwen, and Liesbet Van der Perre. 2020. "Low-Complexity Design and Validation of Wireless Motion Sensor Node to Support Physiotherapy" Sensors 20, no. 21: 6362. https://doi.org/10.3390/s20216362