**4. Conclusion**

range, although the user can adjust that range or calibrate the range for variable lighting

While segmentation is not mandatory, the results of the colour analysis component are much more accurate if done on a segmented image, as this allows the algorithm to disregard the background (**Figure 14** images taken from http://reference.medscape.com/features/slideshow/

Users can also consider hue, saturation, value (HSV) and red‐yellow‐black (RYB) formats for colour analysis. Hue, saturation, value (HSV) format responds to lighting, and as such, it may be a good option when one wants to tune the colour more specifically. RYB (red‐yellow‐black) has a fitting relationship to wound stages, and RGB results can be converted to RYB. The approximate ratios of red, yellow, and black correlated to wound stages are shown in **Figure 15**. Wound stages I and II rely only on red, but are differentiated on the intensity of the red in the image. The subsequent wound stages are differentiated on the proportions of each of the three colours in the image. The error inherent in this method depends to some extent on the definitions of colours set by the user. A recommendation is to associate this component with a machine learning component, once a large enough data set is collected. In this way,

Finally, expert systems can be developed to determine wound stages from the RGB and/or RYB data. This again relies on collecting a sufficiently large data set. Alternatively, support vector machine (VSM) or another machine learning algorithm can be applied to determine the

conditions.

pressure‐ulcers).

136 Mobile Health Technologies - Theories and Applications

colour parameters can be more precisely defined.

**Figure 14.** Histogram results before and after segmentation.

SmartWoundCare as a mobile wound management prototype demonstrates the wide rele‐ vance of mHealth for applications within healthcare facilities and their integration with larger EMR and eHealth systems, as well as the application of telehealth to connect underserved communities. Community health and home‐based care is an equally important and in some way a more urgent implementation. For example, nurses of the Winnipeg Regional Health Authority alone carry out 450,000 wound visits per year in its Home Care program in clients' homes. Particularly in home‐based care, the integration of SmartWoundCare with a suite of mHealth tools is a natural extension. A logical partner app for SmartWoundCare is diabetes monitoring, as well as novel pre‐emptive applications such as an early warning system for injury or damage to diabetic feet due to neuropathy [43].

SmartWoundCare and other mHealth applications also illuminate opportunities in Big Data, in which a community of users generate data – in this case, a wound database – from which relevant trends in wound diagnosis and healing can be extracted and form part of the body of knowledge in wound care.
