**Acknowledgements**

We thank our colleague Jaime Valderrama and Juan David Arango for the support and time invested in the current research job.

**15**

**Author details**

Yor Jaggy Castaño-Pino1

provided the original work is properly cited.

2 Fundación Valle del Lili, Cali, Colombia

*Using Wavelets for Gait and Arm Swing Analysis DOI: http://dx.doi.org/10.5772/intechopen.84962*

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*, Beatriz Muñoz2

and Jorge Luis Orozco2

, Andrés Navarro1

1 i2t Research Team, Icesi University, Cali, Colombia

\*Address all correspondence to: anavarro@icesi.edu.co

*Using Wavelets for Gait and Arm Swing Analysis DOI: http://dx.doi.org/10.5772/intechopen.84962*

*Wavelet Transform and Complexity*

The use of Kinect® in this clinical context has reported relative and overall reliability regarding spatiotemporal parameters [45–47]. Further advances in software and hardware are essential to further enhance Kinect's® sensitivity for kinematic measurements [48, 49]. Nevertheless, since the RGBD cameras, like Kinect, are low-cost and portable devices, this represents an opportunity in the field of telemedicine, allowing easy access to gait assessment in the clinical space and allowing remote diagnose in rural areas, where there are no clinical experts. Finally, it is remarkable how the eMotion Capture system can calculate and automatically obtain the gait cycle variables that are considered relevant for decision-making processes in the clinical context of patients with

Some authors propose that arm swing analysis could help in the differentiation between TD and PIG subtypes, but the small sample in this study limited the subgroup analysis. Because Kinect was discontinued from the market, in future research jobs, alternative RGBD cameras, such as the Intel RealSense D435, needs to

From the previous sections, the use of wavelet techniques for gait analysis and arm swing was detailed. Finally, we can conclude that it is possible to use wavelet techniques to automate and quantify spatiotemporal variables related to the gait, to perform an objective analysis of Parkinson's disease. In addition to this, the eMotion system has demonstrated to be a useful tool that can be used in a clinical context to generate spatiotemporal variables like arm swing asymmetry, arm swing speed, swing magnitude, stance time, swing time, step number, duration test, and speed, which are useful for differentiating PD patients from healthy

We thank our colleague Jaime Valderrama and Juan David Arango for the sup-

**7. Limitations**

Parkinson's disease.

be investigated.

**8. Conclusion**

individuals.

**Acknowledgements**

port and time invested in the current research job.

**14**
