Pratool Bharti

Detecting self-harming activities with wearable devices

In the United States, there are more than 35, 000 reported suicides with approximately 1, 800 of them being psychiatric inpatients. Staff perform intermittent or continuous observations in order to prevent such tragedies, but a study of 98 articles over time showed that 20% to 62% of suicides happened while inpatients were on an observation schedule. Reducing the instances of suicides of inpatients is a problem of critical importance to both patients and healthcare providers. In this paper, we introduce SHARE - A Self-Harm Activity Recognition Engine, which attempts to infer self-harming activities from sensing accelerometer data using smart devices worn on a subjects wrist. Preliminary classification accuracy of 80% was achieved using data acquired from 4 subjects performing a series of activities (both self-harming and not). The results, application, and proposed technology platform are discussed in-depth.

On the Feasibility of Leveraging Smartphone Accelerometers to Detect Explosion Events

In this paper, we investigate the feasibility of leveraging the accelerometer in modern smartphones to detect the triggering of explosion events. By emplacing a static smartphone and a state-of-the-art seismometer in the vicinity of real explosion blasts (conducted at an Explosives Research Lab in a university setting), and comparing their detected event readings, we make several insightful contributions. We find that readings from events in the smartphone and the seismometer are highly correlated in the temporal and frequency domain. We then demonstrate the feasibility of designing an algorithm in the smartphone (executing as an app) to detect the triggering of an explosion based on comparing short term sudden spikes in vibrations due to an explosion event, and long-term dormancy in vibration readings (in the absence of an explosion). To the best of our knowledge, ours is the first work that demonstrates the feasibility of leveraging smartphones for detecting explosion events.

Determining the effectiveness of soil treatment on plant stress using smart-phone cameras

Plants are vital to the health of our biosphere, and effectively sustaining their growth is fundamental to the existence of life on this planet. A critical aspect, which decides the sustainability of plant growth is the quality of soil. All other things being fixed, the quality of soil greatly impacts the plant stress, which in turn impacts overall health. Although plant stress manifests in many ways, one of the clearest indicators are colors of the leaves. In this paper, we conducted an experimental study in a greenhouse for detecting plant stress caused by nutrient deficiencies in soil using smart-phone cameras, coupled with image processing and machine learning algorithms. The greenhouse experiment was conducted by growing two plant species; willows (Salix Pentandra) and poplars (Populus deltoides x nigra, DN34), in two treatments. These treatments included: unamended tailings (collected from a lead mine tailings pond and characterized by nutrient deficiency), and biosolids amended tailings. Biosolids are very rich in nutrients and were added to the tailings in one of the two treatments to supply plants with nutrients. Subsequently, we captured various images of plant leaves grown in both soils. Each image taken was pre-processed via filtration to remove associated noise, and was segmented into pixels to facilitate scalability of analysis. Subsequently, we designed random forests based algorithms to detect the stress of leaves as indicated by their coloring. In a dataset consisting of 34 leaves, our technique yields classifications with a high degree of prediction, recall and F1 score. Our work in this paper, while restricted to two types of plants and soils, can be generalized. We see applications in the emerging area of urban farming in terms of empowering citizens with tools and technologies for enhancing quality of farming practices.

Identifying mosquito species using smart-phone cameras

Mosquito borne diseases have been amongst the most important healthcare concerns since time. An important component in combating the spread of infections in any geographic region of interest has been to identify the type of species that are prevalent in that region. As of today, dedicated personnel are assigned in most (if not all nations) to trap samples and identify them. Unfortunately, the process of identifying the actual species of mosquito is currently a manual process requiring highly trained personnel to visually inspect each specimen one by one under a microscope to make the identification. In this paper, we propose a system to automate this process. Specifically, we demonstrate results of an experiment we conducted where learning algorithms were designed to process images of captured mosquito samples taken via a smart-phone camera in order to identify the actual species. Using a total sample size of 60 images that included 7 species collected by the Hillsborough County Mosquito and Aquatic Weed Control Unit (in the city of Tampa) our proposed technique using Random Forests achieved an overall accuracy of 83:3% in correctly identifying the species of mosquito with good precision and recall. While our proposed technique will greatly benefit the state-of-the-art in species identification, we also believe that common citizens can also use our proposed system to improve existing mosquito control programs across the globe.

Leveraging multi-modal smartphone sensors for ranging and estimating the intensity of explosion events

Abstract Our society, unfortunately, is increasingly becoming exposed to explosion events that could have serious consequences. While explosion events like intentionally triggered bombs cause obvious harm to life and property, other explosions intended for benign purposes in quarries and construction zones may also cause unintended harm as a result of emanating seismic vibrations. As of today, detecting explosions, ranging them, and estimating their intensities are all accomplished only by seismometers that sense the associated ground vibrations and pressure changes as a result of their triggering. Unfortunately, seismometers are bulky, expensive and unsuitable for the ubiquitous use. In this paper, our broad motivation is to demonstrate the feasibility of leveraging the pervasive sensing and processing capabilities of modern smartphones to analyze explosion events. Within this context, we specifically address the problem of ranging and estimating the intensity of an explosion by leveraging the accelerometer and pressure sensors in the smartphone. To do so, we emplaced a number of smartphones in the vicinity of real explosion blasts conducted at a university mining laboratory, where the material blasted was Dynamite with Ammonium Nitrate Fuel Oil (ANFO). We then collected the corresponding accelerometer and pressure readings sensed by the phone. We extracted a number of novel features, and designed a machine learning based algorithmic framework for ranging and estimating the intensity of the explosion event. After an extensive validation, we find that the average-case error in ranging (i.e., estimating the distance to the source of the explosion event) and estimating the intensity of explosive material (in terms of its charge weight) are 8.24% and 7.37%, respectively. We also present perspectives on encoding our algorithm as a smartphone app, identify several critical challenges that will be encountered in real-time data processing of smartphone accelerometers and pressure sensors in the context of pervasive sensing of explosions, and also identify other practical issues like the diversity of smartphones. To the best of our knowledge, our work is pioneering in demonstrating the feasibility of using smartphones to analyze explosion events. We believe there are significant societal benefits emanating from our work.