IoT Based Pain Management using Physiological Parameters

The continuous technology evaluation is benefiting our lives to a great extent. The evolution of Internet of things and deployment of wireless sensor networks is making it possible to have more connectivity between people and devices used extensively in our daily lives. Almost every discipline of daily life including health sector, transportation, agriculture etc. is benefiting from these technologies. There is a great potential of research and refinement of health sector as the current system is very often dependent on manual evaluations conducted by the clinicians. There is no automatic system for patient health monitoring and assessment which results to incomplete and less reliable heath information. Internet of things has a great potential to benefit health care applications by automated and remote assessment, monitoring and identification of diseases. Acute pain is the main cause of people visiting to hospitals. An automatic pain detection system based on internet of things with wireless devices can make the assessment and redemption significantly more efficient. The contribution of this research work is proposing pain assessment method based on physiological parameters. The physiological parameters chosen for this study are heart rate, electrocardiography, breathing rate and galvanic skin response. As a first step, the relation between these physiological parameters and acute pain experienced by the test persons is evaluated. The electrocardiography data collected from the test persons is analyzed to extract interbeat intervals. This evaluation clearly demonstrates specific patterns and trends in these parameters as a consequence of pain. This parametric behavior is then used to assess and identify the pain intensity by implementing machine learning algorithms. Support vector machines are used for classifying these parameters influenced by different pain intensities and classification results are achieved. The classification results with good accuracy rates between two and three levels of pain intensities shows clear indication of pain and the feasibility of this pain assessment method. An improved approach on the basis of this research work can be implemented by using both physiological parameters and electromyography data of facial muscles for classification.

Neck Pain in Adolescent Headache Sufferers. A Cohort Study of Schoolchildren

Although neck pain (NP) and headache (HA) are often concomitant in adolescents, few data exist on the association of NP with HA in this age group. The aim of the study was to examine the association of concomitant NP with adolescent HA and with the outcome of HA. The associations of self-reported NP, physical findings of the neck and disc degeneration of the cervical spine with adolescent HA were studied. This study is part of a population-based follow-up study of 12-year-old children (N 1135/1409) with and without HA. A sample of adolescents (N = 304) was followed to the age of 16 years. At the age of 17 years, 69 of them participated in a magnetic resonance imaging (MRI) study of the cervical spine. During the follow-up from 13 to 16 years of age, changes in both HA type and frequency were common. A poor outcome of HA was associated with NP interfering with daily activities at the age of 13 years. The changes in HA type were not predictable by NP. At the age of 16 years, local and referred palpation pain of the neck muscles, self-reported NP and NP intensity were associated with HA, and especially with disturbing HA unresponsive to analgesics. The association of NP with HA was not determined by HA type. Mild degenerative changes of the cervical spine were common but did not contribute to headache. HA in adolescence is often episodic, and prevention and treatment of NP could be important in the prevention of future chronic adult HA.