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Ramnani, R.

Publications and source records attributed to Ramnani, R..

2 recordsLinked to original sources

A Pilot Study: Development of a Reference Spatiotemporal Gait Data Set for Indian Subjects.

1.Interpretation of pathological gait has, for decades, offered insight into amputee anomalies and diseases such as cerebral palsy. Gait analysis has been actively used at the Bhagwan Mahaveer Viklang Sahatya Samiti for such purposes. The normative gait data used to compare data obtained from amputees, however, has been collected from laboratories under dissimilar conditions, skewing interpretation. Spatiotemporal gait parameters were extractedfrom 43 male Indian subjects using a 7.5m walkway and the BTS Bioengineering GAITLAB setup. Working under the hypothesis that a lack of cross-cultural validity was somewhat responsible for variations in normative gait, we attempted to develop a region-specific data set for use at the headquarters of the Jaipur Foot Organization Bhagwan Mahaveer Viklang Sahatya Samiti (BMVSS). Stratified random sampling was used to recruit subjects and measures were taken to ensure minimal effects of extraneous variables. Statistical analysis was performed on obtained data using one-way analysis of variance (ANOVA) to gauge the effect age and ethnicity had on normative values of the parameters investigated. We found statistically significant p-values for a few spatiotemporal parameters in the analysis of variance for both age and ethnicity. While the results were much less significant than initially hypothesized, the study proved an efficient way to create a normative gait data set exclusively for use at the gait laboratory of the Jaipur Foot Organization, thereby eliminating potential erroneous interpretation of pathological gait when comparing said gait to normative gait data obtained in laboratories under dissimilar conditions.

bioengineering

A Probabilistic Approach to the Detection and Diagnosis of Obstructive Sleep Apnea and Its Environmental Factors Using Chest Movement Analysis, Recursive Noise Reduction and Machine Learning Algorithms.

1In a novel approach to diagnose Obstructive Sleep Apnea, electronic components, such as an Arduino Mega, a Bluetooth Transceiver, an accelerometer, and a air-quality sensor, were put together to create a wearable that would detect the frequency of apnea events, detect and diagnose the disorder, and sound an alarm when necessary. A primary consideration was to make the mechanism accessible and affordable, and in doing so, lower the cases of Obstructive Sleep Apnea that go undiagnosed due to the cost and inconvenience associated with the traditional diagnosis method--a Polysomnogram. Bluetooth capability was an additional consideration so that the device would transmit data directly to an android smartphone, eliminating the need for an additional output mechanism. The total cost of the device, quite surprisingly, did not exceed $30, and therein rendered the device an accessible, affordable mechanism for diagnosis. Tests of the device on diagnosed patients yielded data consistent with the diagnosis, with a few false positives as a result of the excessive sensitivity of the sensors.

biophysics