Raw Versus Linear Acceleration in the Recognition of Wrist Motions Related to Eating During Everyday Life

This thesis investigates the difference between raw and linear acceleration in wrist motion for detecting eating episodes. In previous work, our group developed a classifier that analyzed linear motion and achieved good accuracy. However, the classifier can be volatile in the sense that when retrained and tested on the same data, accuracy varies, especially when trained on small amounts of data such as for a single individual. We hypothesize that in part this may be due to the noise in linear acceleration which is significantly larger relative to normal human wrist motions as compared to the noise in raw acceleration. We therefore perform a set of experiments to determine if classifier accuracy and/or stability can be improved by analyzing raw acceleration instead of linear acceleration. The dataset used for this work is the Clemson All-Day Eating (CAD) dataset. This was collected over a period of one year, in 2014. In the process of data collection, 351 participants were recruited and 354 days of wrist data was recorded. The recorded data contained 1,133 meals spread over 250 hours of eating. The total length of the recorded data was nearly 4,680 hours. In this work, the CAD dataset was reduced to 342 days and 1034 meals because for some recordings, raw acceleration data was not saved. Previous work developing a classifier based on linear acceleration achieved a time-based weighted accuracy of 80%, a true positive rate of 89% on eating episodes, and a false positive per true positive rate of 1.7. However, these results were based upon a single run of train and test. Recently we discovered that the model accuracy varies somewhat between runs. We therefore perform a replication experiment on the linear classifier to confirm these results by rerunning the entire experiment 10 times. We report the average and standard deviation of all metrics across these runs. This helps establish a better baseline for comparison of our new classifier that analyzes raw acceleration. We next analyze the same set of data, using the same neural network model and general approach as for the linear acceleration-based classifier, to compare its accuracy and stability. Evaluating all results, we found that the linear acceleration classifier achieved (average ± standard deviation across 10 runs) a TPR of 86% ±1.2% and a FP/TP of 1.7 ± 0.3. It also achieved a weighted accuracy of 79 % ± 0.5 %. Thus, we concluded that the results of original experiment were above the average results and could either be due to a freak training and testing run or due to contamination of the testing data. These results set up a new baseline with which we compare the raw acceleration model metrics. We found that the raw acceleration achieved a TPR of 84% ± 1.3% and a FP/TP of 1.7 ± 0.3. In the case of time metrics, the raw acceleration model achieved a weighted accuracy of 78% ± 0.4%. Thus, on average, we found that the linear acceleration performed slightly better than raw acceleration in episode detection. The time metrics for both raw and linear acceleration were more or less similar but we did see a higher standard deviation for the raw models. Our results indicate that linear acceleration does provide greater accuracy than raw acceleration. Even though raw acceleration has a higher signal-to-noise ratio than linear acceleration, in terms of normal human wrist motions, our classifier model has relatively equal volatility when analyzing either signal. We conclude that the main source of model volatility is still unknown. Thus, we found that linear acceleration is, overall, a better predictor of eating as compared to raw acceleration. It should be noted that the difference in the accuracies is very minor and the volatility in the training process could account for some of the differences

Quantitative Functional Imaging and Dosimetry of Photodynamic Therapy: Clinical Feasibility of Performing Dosimetric Measurements in Non-melanoma Skin Cancer Patients

Photodynamic therapy (PDT) has emerged as a viable alternative to traditional treatment regimes in the treatment of non-melanoma skin cancer. However, approximately 20-25% of patients receiving PDT exhibit heterogeneous response or tumour recurrence. This inconsistency can be attributed to generic treatment protocols and lack of planning, monitoring, and assessment of treatment response. A personalized approach is required to increase treatment efficacy and reduce response variability. To this effect, the work presented in this thesis outlines the development and characterization of: (a) fluorescence- enabled spatial frequency domain imaging system for monitoring the photobleaching response of PpIX; and (b) statistical variance method for monitoring the vascular response in high-frequency ultrasound imaging. Preliminary results from the clinical implementation of this quantitative functional imaging system are also presented. With time, this system can be integrated into the clinical workflow to enable the creation of a personalized treatment plan – improving treatment efficacy and clinical outcomes.M.A.S

Evaluation of Deep Learning Models for Multi-Step Ahead Time Series Prediction

Time series prediction with neural networks has been the focus of much research in the past few decades. Given the recent deep learning revolution, there has been much attention in using deep learning models for time series prediction, and hence it is important to evaluate their strengths and weaknesses. In this paper, we present an evaluation study that compares the performance of deep learning models for multi-step ahead time series prediction. The deep learning methods comprise simple recurrent neural networks, long short-term memory (LSTM) networks, bidirectional LSTM networks, encoder-decoder LSTM networks, and convolutional neural networks. We provide a further comparison with simple neural networks that use stochastic gradient descent and adaptive moment estimation (Adam) for training. We focus on univariate time series for multi-step-ahead prediction from benchmark time-series datasets and provide a further comparison of the results with related methods from the literature. The results show that the bidirectional and encoder-decoder LSTM network provides the best performance in accuracy for the given time series problems

A Strategy Design Analysis of the Toronto Poverty Reduction Strategy

Poverty reduction strategies have become a popular policy instrument for addressing poverty across various levels of government. In 2015, the City of Toronto launched phase one of its own municipal poverty reduction strategy, which ran from 2015 to 2018. The following commentary uses strategy design principles to examine the strengths and weaknesses of phase one of the Toronto Poverty Reduction Strategy (TPRS) based on interviews conducted with four key stakeholders involved in the strategy’s design and implementation. Joined-up governance and public participation were both identified as design strengths of the TPRS, while a lack of prioritization and funding were identified as challenges to effective implementation. As governments across Canada and the world search for feasible, acceptable, and effective ways to reduce and alleviate poverty and other health-related issues. strategy design principles provide a valuable framework for analyzing the complex processes which contribute to a strategy’s success or failure

The pedicle screw accuracy using a robotic system and measured by a novel three-dimensional method

Abstract Robotics in medicine is associated with precision, accuracy, and replicability. Several robotic systems are used in spine surgery. They are all considered shared-control systems, providing "steady-hand" manipulation instruments. Although numerous studies have testified to the benefits of robotic instrumentations, they must address their true accuracy. Our study used the Mazor system under several situations and compared the spatial accuracy of the pedicle screw (PS) insertion and its planned trajectory. We used two cadaveric specimens with intact spinal structures from C7 to S1. PS planning was performed using the two registration methods (preopCT/C-arm or CT-to-fluoroscopy registration). After planning, the implant spatial orientation was defined based on six anatomic parameters using axial and sagittal CT images. Two surgical open and percutaneous access were used to insert the PS. After that, another CT acquisition was taken. Accuracy was classified into optimal, inaccurate and unacceptable according to the degree of screw deviation from its planning using the same spatial orientation method. Based on the type of spatial deviation, we also classified the PS trajectory into 16 pattern errors. Seven (19%) out of 37 implanted screws were considered unacceptable (deviation distances > 2.0 mm or angulation > 5°), and 14 (38%) were inaccurate (> 0.5 mm and ≤ 2.0 mm or > 2.5° and ≤ 5°). CT-to-fluoroscopy registration was superior to preopCT/C-arm (average deviation in 0.9 mm vs. 1.7 mm, respectively, p < 0.003), and percutaneous was slightly better than open but did not reach significance (1.3 mm vs. 1.7 mm, respectively). Regarding pattern error, the tendency was to have more axial than sagittal shifts. Using a quantitative method to categorize the screw 3D position, only 10.8% of the screws were considered unacceptable. However, with a more rigorous concept of inaccuracy, almost half were non-optimal. We also identified that, unlike some previous results, the O-arm registration delivers more accurate implants than the preopCT/C-arm method

Watson on the farm: using cloud-based artificial intelligence to identify early indicators of water stress

As demand for freshwater increases while supply remains stagnant, the critical need for sustainable water use in agriculture has led the EPA Strategic Plan to call for new technologies that can optimize water allocation in real-time. This work assesses the use of cloud-based artificial intelligence to detect early indicators of water stress across six container-grown ornamental shrub species. Near-infrared images were previously collected with modified Canon and MAPIR Survey II cameras deployed via a small unmanned aircraft system (sUAS) at an altitude of 30 meters. Cropped images of plants in no, low-, and high-water stress conditions were split into four-fold cross-validation sets and used to train models through IBM Watson’s Visual Recognition service. Despite constraints such as small sample size (36 plants, 150 images) and low image resolution (150 pixels by 150 pixels per plant), Watson generated models were able to detect indicators of stress after 48 hours of water deprivation with a significant to marginally significant degree of separation in four out of five species tested (p < 0.10). Two models were also able to detect indicators of water stress after only 24 hours, with models trained on images of as few as eight water-stressed Buddleia plants achieving an average area under the curve (AUC) of 0.9884 across four folds. Ease of pre-processing, minimal amount of training data required, and outsourced computation make cloud-based artificial intelligence services such as IBM Watson Visual Recognition an attractive tool for agriculture analytics. Cloud-based artificial intelligence can be combined with technologies such as sUAS and spectral imaging to help crop producers identify deficient irrigation strategies and intervene before crop value is diminished. When brought to scale, frameworks such as these can drive responsive irrigation systems that monitor crop status in real-time and maximize sustainable water use.This work was partially supported by a grant from the J. Frank Schmidt Family Charitable Foundation and is based on work supported by NIFA/USDA under project numbers SC-1700540, SC-1700543 and 2014-51181-22372 (USDA-SCRI Clean WateR3). Research of Drs. Peña and de Castro was financed by the “Salvador de Madariaga” for Visiting Researchers in Foreign Centers Program (Spanish MICINN funds) and the Intramural-CSIC Project (ref. 201940E074), respectively.Peer reviewe