FreeWalker: a smart insole for longitudinal gait analysis

A Reliability Study of the Load Distribution Percentage While Walking Using Curalgia Feet Sx Smart Insoles

Background Gait analysis has evolved through many years of research. Many methods are used to analyze the gait of a subject. Recent times have shown a high demand for wearable sensor-based insoles integrated with smartphone-based devices used for gait analysis due to ease of use. This study utilized Curalgia Feet Sx Smart Insoles and its software toolset, Gait Analysis+, designed and manufactured in India making it an accessible and cost-effective option. The Curalgia Feet Sx Smart Insoles allow for a broad range of biofeedback-based rehabilitation and recovery training for several patients and have many applications, such as sports performance enhancement and neurological disorder rehab (e.g., brain stroke rehab). The system also significantly delays the onset of neurodegenerative illnesses by providing balance and proprioceptive training. The smart insole can help the athlete, the coach, and the sports medicine team get the on-field data in real-time, which will help them understand if any technical or biomechanical alterations are required. This may help in performance enhancement. This study aimed to determine the interrater reliability of the load distribution percentage parameter of the Curalgia Feet Sx Smart Insole for both feet while walking in a controlled setting. Methodology A total of 120 subjects were enrolled in the study. In total, 90 subjects were randomly selected using Research Randomizer which included male and female students and staff at Sardar Bhagwan Singh University. The subjects were asked to come to the research lab of the physiotherapy department wearing their sports shoes. Curalgia Feet Sx insoles were inserted into the shoe firmly to fit properly. Two assessors took two readings after the smart insole was connected to the smartphone-based application, GaitAnalysis+, via Bluetooth. The dynamic analysis option was selected, and each subject's analysis was done one after another with a desirable break in between. Each subject walked for three minutes at their normal speed after pressing "Start Analysis." At the three-minute mark, the subjects were asked to press "Stop Analysis" and the investigator downloaded the report on the smartphone. The data collected was compiled as the cumulative weight in kg (load distribution) borne and the % weight (load distribution %) borne by each foot for the duration of the walk. Statistical analysis was done using Karl Pearson's test and interclass correlation calculation. Results Assessor 1 and Assessor 2 collected readings for the left foot as "L" and the right foot as "R." Assessor 1 readings were L1-R1 for load distribution and L1% and R1% for load distribution %. Assessor 2 readings were L2-R2 for load distribution and L2% and R2% for load distribution %. The r value (correlation coefficient) was calculated using the load distribution. The mean value of L1 was 337.46 (SD=94.16). The mean L2 was 313.6 (SD=104.40). The R1 mean was 229.03 (SD=112.88), and the R2 mean was 233.011 (SD=79.84). The r was 0.7171 for the left foot and 0.7502 for the right foot, suggesting an excellent correlation. The ICC was calculated for load distribution %. The means of L1% was 55.94, L2% was 57.59, R1% was 44.06, and R2% was 42.41. The ICC was found to be 0.91 for both feet, suggesting high interrater reliability for the tested parameter. Conclusions The findings confirmed that the Curalgia Feet Sx Smart Insoles presented good interrater reliability for the load distribution % parameter.

Towards remote healthcare monitoring using accessible IoT technology: state-of-the-art, insights and experimental design

Healthcare studies are moving toward individualised measurement. There is need to move beyond supervised assessments in the laboratory/clinic. Longitudinal free-living assessment can provide a wealth of information on patient pathology and habitual behaviour, but cost and complexity of equipment have typically been a barrier. Lack of supervised conditions within free-living assessment means there is need to augment these studies with environmental analysis to provide context to individual measurements. This paper reviews low-cost and accessible Internet of Things (IoT) technologies with the aim of informing biomedical engineers of possibilities, workflows and limitations they present. In doing so, we evidence their use within healthcare research through literature and experimentation. As hardware becomes more affordable and feature rich, the cost of data magnifies. This can be limiting for biomedical engineers exploring low-cost solutions as data costs can make IoT approaches unscalable. IoT technologies can be exploited by biomedical engineers, but more research is needed before these technologies can become commonplace for clinicians and healthcare practitioners. It is hoped that the insights provided by this paper will better equip biomedical engineers to lead and monitor multi-disciplinary research investigations.

Sensor Fusion and Smart Sensor in Sports and Biomedical Applications

The following work presents an overview of smart sensors and sensor fusion targeted at biomedical applications and sports areas. In this work, the integration of these areas is demonstrated, promoting a reflection about techniques and applications to collect, quantify and qualify some physical variables associated with the human body. These techniques are presented in various biomedical and sports applications, which cover areas related to diagnostics, rehabilitation, physical monitoring, and the development of performance in athletes, among others. Although some applications are described in only one of two fields of study (biomedicine and sports), it is very likely that the same application fits in both, with small peculiarities or adaptations. To illustrate the contemporaneity of applications, an analysis of specialized papers published in the last six years has been made. In this context, the main characteristic of this review is to present the largest quantity of relevant examples of sensor fusion and smart sensors focusing on their utilization and proposals, without deeply addressing one specific system or technique, to the detriment of the others.