A practical design and implementation of a low cost platform for remote monitoring of lower limb health of amputees in the developing world

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In many areas of the world accessing professional physicians ‘when needed/as needed’ might not be always possible for a variety of reasons. Therefore, in such cases a targeted e-Health solution to safeguard patient long-term health could be a meaningful approach. Today’s modern healthcare technologies, often built around electronic and computer-based equipment, require an access to a reliable electricity supply. Many healthcare technologies and products also presume access to the high speed internet is available, making them unsuitable for use in areas where there is no fixed-line internet connectivity, access is slow, unreliable and expensive, yet where the most benefit to patients may be gained.
In this paper a full mobile sensor platform is presented, based around readily-purchased consumer components, to facilitate a low cost and efficient means of monitoring the health of patients with prosthetic lower limbs. This platform is designed such that it can also be operated in a standalone mode i.e. in the absence of internet connectivity, thereby making it suitable to the developing world. Also, to counter the challenge of power supply issues in e-Health monitoring, a self-contained rechargeable solution to the platform is proposed and demonstrated. The platform works with an Android mobile device, in order to allow for the capture of data from a wireless sensor unit, and to give the clinician access to results from the sensors. The results from the analysis, carried out within the platform’s Raspberry Pi Zero, are demonstrated to be of use for remote monitoring. This is specifically targeted for monitoring the tissue health of lower limb amputees. The monitoring of residual limb temperature and gait can be a useful indicator of tissue viability in lower limb amputees especially those suffering from diabetes. We describe a route wherein non-invasive monitoring of tissue health is achievable using the Gaussian process technique. This knowledge will be useful in establishing biomarkers related to a possible deterioration in a patient’s health or for assessing the impact of clinical interventions.
Original languageEnglish
Pages (from-to)7440 - 7451
Number of pages12
JournalIEEE Access
Publication statusPublished - 27 Oct 2016


  • accelerometer
  • e-health
  • elastomer
  • gait
  • Gaussian processes for machine learning (GPML)
  • gyroscope
  • lower limb prosthetics
  • rehabilitation
  • sensors
  • tissue Health
  • wearable sensor platform


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