archie gupta - Academia.edu (original) (raw)
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Papers by archie gupta
Wearable Augmented Reality (AR) devices1 are being explored in many applications for visualizing ... more Wearable Augmented Reality (AR) devices1 are being explored in many applications for visualizing real-time contextual information. More importantly, these devices can also be used in tele-assistance from remote sites when on-field operators require off-field expert's guidance for trouble-shooting. For an effective communication, touchless hand gestures are the most intuitive to select a Region Of Interest (ROI) like defective parts in a machine, through a wearable. This paper presents a hand gestural interaction method to localize the ROI in First Person View (FPV). The region selected using freehand sketching gestures is highlighted to the remote server setup for expert's advice. Novelty of the proposed method include (a) touchless fingerbased gesture recognition algorithm that runs on smartphones, which can be used with wearable frugal modality like Google Cardboard/Wearality, (b)reducing the network latency and achieving real-time performance by on-board implementation of...
Sleep quantity affects an individual's personal health. The gold standard of measuring sleep ... more Sleep quantity affects an individual's personal health. The gold standard of measuring sleep and diagnosing sleep disorders is Polysomnography (PSG). Although PSG is accurate, it is expensive and it lacks portability. A number of wearable devices with embedded sensors have emerged in the recent past as an alternative to PSG for regular sleep monitoring directly by the user. These devices are intrusive and cause discomfort besides being expensive. In this work, we present an algorithm to detect sleep using a smartphone with the help of its inbuilt accelerometer sensor. We present three different approaches to classify raw acceleration data into two states - Sleep and Wake. In the first approach, we take an equation from Kushida's algorithm to process accelerometer data. Henceforth, we call it Kushida's equation. While the second is based on statistical functions, the third is based on Hidden Markov Model (HMM) training. Although all the three approaches are suitable for a...
Wearable Augmented Reality (AR) devices1 are being explored in many applications for visualizing ... more Wearable Augmented Reality (AR) devices1 are being explored in many applications for visualizing real-time contextual information. More importantly, these devices can also be used in tele-assistance from remote sites when on-field operators require off-field expert's guidance for trouble-shooting. For an effective communication, touchless hand gestures are the most intuitive to select a Region Of Interest (ROI) like defective parts in a machine, through a wearable. This paper presents a hand gestural interaction method to localize the ROI in First Person View (FPV). The region selected using freehand sketching gestures is highlighted to the remote server setup for expert's advice. Novelty of the proposed method include (a) touchless fingerbased gesture recognition algorithm that runs on smartphones, which can be used with wearable frugal modality like Google Cardboard/Wearality, (b)reducing the network latency and achieving real-time performance by on-board implementation of...
Sleep quantity affects an individual's personal health. The gold standard of measuring sleep ... more Sleep quantity affects an individual's personal health. The gold standard of measuring sleep and diagnosing sleep disorders is Polysomnography (PSG). Although PSG is accurate, it is expensive and it lacks portability. A number of wearable devices with embedded sensors have emerged in the recent past as an alternative to PSG for regular sleep monitoring directly by the user. These devices are intrusive and cause discomfort besides being expensive. In this work, we present an algorithm to detect sleep using a smartphone with the help of its inbuilt accelerometer sensor. We present three different approaches to classify raw acceleration data into two states - Sleep and Wake. In the first approach, we take an equation from Kushida's algorithm to process accelerometer data. Henceforth, we call it Kushida's equation. While the second is based on statistical functions, the third is based on Hidden Markov Model (HMM) training. Although all the three approaches are suitable for a...