Smartphones, 4G and 5G: an update on advances in IoT for orthopaedics

Smartphones, 4G and 5G: an update on advances in IoT for orthopaedics

Last year, Peter Ogrodnik wrote about the potential for Internet of Things in orthopaedics and the barriers to implementation [1,2] . In this article he would like to expand the concept with detail about a potential application as an example.


Patients that present to A&E with ankle fractures often arrive with severe swelling (oedema). This swelling prohibits immediate surgical intervention due to increased risk of complications [3,4]. The common treatment is to apply a temporary stabilisation and the patient is either admitted as an in-patient or sent home with instructions to keep the limb elevated. However, many studies have shown [3,4] that sending the patients home often results in them not following instruction. As a consequence, when the patients arrive for surgery, the swelling may not have subsided and the clinical team need to cancel the case at the last minute and have little chance of filling, an expensive theatre slot. Therefore, many patients are kept on the ward, with their limb elevated for an average of 4.5 days. Releasing this number of bed days is akin to creating 3 average sized hospitals with no capital cost (imagine how many bed days would be released for all similar bed-stay events, pre-operative or post-op). Some groups have attempted to use paper-based instructions to replace bed days [3,4,5], but their protocols have not been widely adopted.

What is required is a device that both monitors the limb’s elevation but also provides feedback to both clinician and patient.

Figure 1 illustrates a potential system (as seen in a previous publications [1,2]).The basic premise is simple. A device measures the angle of the limb at prescribed time intervals. If this angle is below the desired / set angle , then a light flashes on the device: an immediate reminder to the patient to elevate the limb.

This data is collected, on board, for the whole day and the whole treatment cycle and transmitted to the ‘cloud’ at a convenient time. This data is now visible to the clinician to decide whether the patient is:

a) Following instructions

b) Needing some further instruction to keep them on track


c) Required to be called in for a pre-operative stay on a ward.

Fig. 2 – Illustration of data depicting subject compliance and non-compliance


Examples of said data are illustrated in Figure 2. In figure 2(a) it is clear that the patient’s limb has been elevated for substantial periods of time. Figure 2(b) illustrates a patient profile that is not demonstrative of following instructions. It is obvious, that as the data on the cloud grows Artificial Intelligence can start to play a role in, for example, stating the optimum angle for a particular patient to maximise the chance of reduced swelling: or identifying patients who are likely to not follow instructions from early data. All is possible once the data set is developed and populated.

How the data is written to the cloud is a point of discussion. Table 1 illustrates the communications networks currently available to the IoT developer. The advantages and disadvantages have been summarised. In the application described earlier use of 3g networks and NBIoT (both via Vodafone) were utilised.


Table 1 – Viable Internet of Things Communication Systems.


The data has to be written to a server. It is worth discussing this technology as it has major implications for healthcare uses.

There are a variety of IoT server systems that can be utilised, but the data has to be stored somewhere. This is of great importance in healthcare. Many countries stipulate that ANY patient data must be stored on a server in their country. Open, easy access systems such as Arduino and AskSensors cannot provide the server location: it is, literally, in the cloud, somewhere. However, higher integrity systems are able to provide an exact location. Amazon (AWS) for example allow you to select a specific location where your data is to be stored. Other providers such as Microsoft, Apple etc. are able to do similar and fortunately for the IoT developer the list of such providers is growing.

In addition, security of the communication method is an important factor: is the system able to write data to the server in an encrypted form? Once received, is the data stored in a secure and robust fashion? In the general population a fear and a misunderstanding of data security is rife [6,7]. Mutual trust and understanding between clinicians, the general public and the IoT solution/m-health providers are essential if this technology is to become widespread in healthcare.

The integrity of the selected server is critical. Is it secure to cyber-attack? Is it secure from a loss of electrical power? All of these would affect your personal computer hence a PC based server in the corner of the office is not a solution unless there is a sophisticated back-up system in operation. One provider I know of is based in an old nuclear bomb shelter!

When considering all this, it is no wonder many companies shy away from IoT in healthcare and that most solutions currently on the market are for the worried-well: a term we adopted to signify the group who present no symptoms but look for them anyway (as opposed to a classic hypochondriac). Does an orthopaedic surgeon want to see reams of ECG data: no, the data has to be specific to the job-in-hand. If not, the benefits will soon be eroded by dissatisfaction. However, as illustrated above the potential savings for correct use of IoT in healthcare is staggering. But there does need to be unification / standardisation for its use to take off. Why would a single hospital want to run multiple IoT systems for multiple devices? Would not a standard protocol be better so that any device could communicate with any server system, in the same way as any CD will play on any CD player?



  1. Ogrodnik, P. (2019) Smartphones,4G and 5G: are there opportunities for orthopaedics? Orthopaedic Product News, 190, 24-26
  2. Ogrodnik, P. (2019) Internet of Things in orthopaedics: are there barriers to implementation? Orthopaedic Product News, 191, 26-27.
  3. Baraza, N., Lever, S. and Dhukaram, V., 2013. Home therapy pathway–Safe and streamlined method of initial management of ankle fractures. Foot and Ankle Surgery, 19(4), pp.250-254.
  4. Khakha, R., Berber, O., Patel, A., Kurar, L. and James, L., 2020. Ankle Home Stay Programme:-A review of ankle fracture management and costs at a busy district general hospital. Annals of Medicine and Surgery, 50, pp.6-9.
  5. Lloyd, JM., Martin, R., Rajagoplan, S., Zieneh, N., and Hartley, R., An innovative and cost-effective way of managing ankle fractures prior to surgery – home therapy. Ann. R. Coll. Surg. Engl., 92 (7) (2010 Oct), pp. 615-618
  6. Aljedaani, B., Ahmad, A., Zahedi, M. and Babar, M.A., 2020. Security Awareness of End-Users of Mobile Health Applications: An Empirical Study. arXiv preprint arXiv:2008.13009.
  7. Prior, S. and Coull, N., 2020, July. Parents unwittingly leak their children’s data: a GDPR time bomb?. In International Conference on Human-Computer Interaction (pp. 471-486). Springer, Cham.



Prof Peter J Ogrodnik is course director for the MSc Medical Engineering Design at Keele University. The statements made in this article do not necessarily reflect those of Keele University. The author recognises the support of the Engineering and Physical Sciences Research Council, Vodafone and the European Space Agency for some of the work leading to this article. Prof Ogrodnik has asked if anyone wishes to work with him and his team on their IoT in healthcare work they can contact him at

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