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8. A Mobile Cloud-Based Parkinson’s Disease Assessment System for Home-Based Monitoring

Author

Pan, Di, Dhall, Rohit, Lieberman, Abraham, and Petitti, Diana B

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundParkinson’s disease (PD) is the most prevalent movement disorder of the central nervous system, and affects more than 6.3 million people in the world. The characteristic motor features include tremor, bradykinesia, rigidity, and impaired postural stability. Current therapy based on augmentation or replacement of dopamine is designed to improve patients’ motor performance but often leads to levodopa-induced adverse effects, such as dyskinesia and motor fluctuation. Clinicians must regularly monitor patients in order to identify these effects and other declines in motor function as soon as possible. Current clinical assessment for Parkinson’s is subjective and mostly conducted by brief observations made during patient visits. Changes in patients’ motor function between visits are hard to track and clinicians are not able to make the most informed decisions about the course of therapy without frequent visits. Frequent clinic visits increase the physical and economic burden on patients and their families. ObjectiveIn this project, we sought to design, develop, and evaluate a prototype mobile cloud-based mHealth app, “PD Dr”, which collects quantitative and objective information about PD and would enable home-based assessment and monitoring of major PD symptoms. MethodsWe designed and developed a mobile app on the Android platform to collect PD-related motion data using the smartphone 3D accelerometer and to send the data to a cloud service for storage, data processing, and PD symptoms severity estimation. To evaluate this system, data from the system were collected from 40 patients with PD and compared with experts’ rating on standardized rating scales. ResultsThe evaluation showed that PD Dr could effectively capture important motion features that differentiate PD severity and identify critical symptoms. For hand resting tremor detection, the sensitivity was .77 and accuracy was .82. For gait difficulty detection, the sensitivity was .89 and accuracy was .81. In PD severity estimation, the captured motion features also demonstrated strong correlation with PD severity stage, hand resting tremor severity, and gait difficulty. The system is simple to use, user friendly, and economically affordable. ConclusionsThe key contribution of this study was building a mobile PD assessment and monitoring system to extend current PD assessment based in the clinic setting to the home-based environment. The results of this study proved feasibility and a promising future for utilizing mobile technology in PD management.

Published
2015
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38. Removal of SiC and Si3N4 inclusions in solar cell Si scraps through slag refining

Author

Li Yaqiong, Zhang Lifeng, and Pan Di

Subjects
si3n4, sic, slag refining, solar cell si scraps, Technology, Chemical technology, TP1-1185, Chemicals: Manufacture, use, etc., TP200-248
Abstract

Silicon was recovered from solar cell Si scraps through 42.5 mol% SiO2–42.5 mol% CaO–15 mol% Al2O3 slag refining. The motion behaviors of Si3N4 and SiC were observed in situ and real-time using a high-temperature laser confocal microscope, and the recovery of Si through slag refining was carried out at 1,500°C for 30 min. Results indicated that both SiC and Si3N4 inclusions were concentrated in the slag, and this work provides a clear framework for recycling and reusing solar cell Si scraps.

Published
2022
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39. Kidney Recovery and Death in Critically Ill Patients With COVID-19–Associated Acute Kidney Injury Treated With Dialysis: The STOP-COVID Cohort Study

Author

Gupta, Hsu, Faugno, Goyal, Raichoudhury, Tariq, Meyer, Sharma, Leaf, Kshirsagar, Walther, Carl P., Anumudu, Samaya J., Arunthamakun, Justin, Kopecky, Kathleen F., Milligan, Gregory P., McCullough, Peter A., Nguyen, Thuy-Duyen, Shaefi, Shahzad, Krajewski, Megan L., Shankar, Sidharth, Pannu, Ameeka, Valencia, Juan D., Bauer, Kenneth A., Waikar, Sushrut S., Kibbelaar, Zoe A., Athavale, Ambarish M., Hart, Peter, Upadhyay, Shristi, Vohra, Ishaan, Oyintayo, Ajiboye, Green, Adam, Rachoin, Jean-Sebastien, Schorr, Christa A., Shea, Lisa, Edmonston, Daniel L., Mosher, Christopher L., Shehata, Alexandre M., Cohen, Zaza, Allusson, Valerie, Bambrick-Santoyo, Gabriela, Bhatti, Noor ul aain, Mehta, Bijal, WilliamsSamantha K. Brenner, Aquino, Walters, Patricia, Go, Ronaldo C., Rose, Keith M., Hernán, Miguel A., Zhou, Amy M., Kim, Ethan C., Lisk, Rebecca, Chan, Lili, Mathews, Kusum S., Coca, Steven G., Altman, Deena R., Saha, Aparna, Soh, Howard, Wen, Huei Hsun, Bose, Sonali, Leven, Emily A., Wang, Jing G., Mosoyan, Gohar, Pattharanitima, Pattharawin, Gallagher, Emily J., Friedman, Allon N., Guirguis, John, Kapoor, Rajat, Meshberger, Christopher, Kelly, Katherine J., Parikh, Chirag R., Garibaldi, Brian T., Corona-Villalobos, Celia P., Wen, Yumeng, Menez, Steven, Malik, Rubab F., Cervantes, Carmen Elena, Gautam, Samir C., Mallappallil, Mary C., Ouyang, Jie, John, Sabu, Yap, Ernie, Melaku, Yohannes, Mohamed, Ibrahim, Bajracharya, Siddhartha, Puri, Isha, Thaxton, Mariah, Bhattacharya, Jyotsna, Wagner, John, Boudourakis, Leon, Nguyen, H. Bryant, Ahoubim, Afshin, Thomas, Leslie F., Sirganagari, Dheeraj Reddy, Guru, Pramod K., Kashani, Kianoush, Tehranian, Shahrzad, Zhou, Yan, Bergl, Paul A., Rodriguez, Jesus, Shah, Jatan A., Gupta, Mrigank S., Kumar, Princy N., Lazarous, Deepa G., Kassaye, Seble G., Melamed, Michal L., Johns. Ryan Mocerino, Tanya S., Prudhvi, Kalyan, Zhu, Denzel, Levy, Rebecca V., Azzi, Yorg, Fisher, Molly, Yunes, Milagros, Sedaliu, Kaltrina, Golestaneh, Ladan, Brogan, Maureen, Thakkar, Jyotsana, Kumar, Neelja, Ross, Michael J., Chang, Michael, Athreya, Akshay, Farag, Mohamed, Schenck, Edward J., Cho, Soo Jung, Plataki, Maria, Alvarez-Mulett, Sergio L., Gomez-Escobar, Luis G., Pan, Di, Lee, Stefi, Krishnan, Jamuna, Whalen, William, Charytan, David, Macina, Ashley, Srivastava, Anand, Leidner, Alexander S., Martinez, Carlos, Kruser, Jacqueline M., Wunderink, Richard G., Hodakowski, Alexander J., Velez, Juan Carlos Q., Price-Haywood, Eboni G., Matute-Trochez, Luis A., Hasty, Anna E., Mohamed, Muner M.B., Avasare, Rupali S., Zonies, David, Al-Samkari, Hanny, Leaf, Rebecca Karp, Rosovsky, Rachel, Sise, Meghan E., Newman, Erik T., Omar, Samah Abu, Pokharel, Kapil K., Sharma, Shreyak, Singh, Harkarandeep, Correa, Simon, Shaukat, Tanveer, Kamal, Omer, Wang, Wei, Lee, Meghan, Strohbehn, Ian A., Li, Jiahua, Mueller, Ariel L., Redfern, Roberta E., Cairl, Nicholas S., Naimy, Gabriel, Abu-Saif, Abeer, Hall, Danyell, Bickley, Laura, Rowan, Chris, Madhani-Lovely, Farah, Peev, Vasil, Reiser, Jochen, Byun, John J., Vissing, Andrew, Kapania, Esha M., Post, Zoe, Patel, Nilam P., Hermes, Joy-Marie, Sutherland, Anne K., Patrawalla, Amee, Finkel, Diana G., Danek, Barbara A., Arikapudi, Sowminya, Paer, Jeffrey M., Cangialosi, Peter, Liotta, Mark, Radbel, Jared, Puri, Sonika, Sunderram, Jag, Scharf, Matthew T., Ahmed, Ayesha, Berim, Ilya, Vatson, Jayanth S., Karp, George, Anand, Shuchi, Levitt, Joseph E., Garcia, Pablo, Boyle, Suzanne M., Song, Rui, Zhang, Jingjing, Woo, Sang Hoon, Deng, Xiaoying, Katz-Greenberg, Goni, Sharshir, Moh’d A., Rusnak, Vadym V., Bansal, Anip, Podoll, Amber S., Chonchol, Michel, Sharma, Sunita, Burnham, Ellen L., Rashidi, Arash, Hejal, Rana, Judd, Eric, Latta, Laura, Tolwani, Ashita, Albertson, Timothy E., Adams, Jason Y., Chang, Steven Y., Beutler, Rebecca M., Schulze, Carl E., Macedo, Etienne, RheeKa, Harin, thleen D. Liu, Jotwani, Vasantha K., Koyner, Jay L., Shah, Chintan V., Jaikaransingh, Vishal, Toth-Manikowski, Stephanie M., Joo, Min J., Lash, James P., Neyra, Javier A., Chaaban, Nourhan, Elias, Madona, Ahmad, Yahya, Dy, Rajany, Iardino, Alfredo, Au, Elizabeth H., Sosa, Marie Anne, Taldone, Sabrina, Contreras, Gabriel, De La Zerda, David, Gershengorn, Hayley B., Fornoni, Alessia, Hayek, Salim S., Blakely, Pennelope, Berlin, Hanna, Azam, Tariq U., Shadid, Husam, Pan, Michael, O’Hayer, Patrick, Meloche, Chelsea, Feroze, Rafey, Padalia, Kishan J., Leya, Jeff, Donnelly, John P., Admon, Andrew J., Flythe, Jennifer E., Tugman, Matthew J., Chang, Emily H., Brown, Brent R., Leonberg-Yoo, Amanda K., Spiardi, Ryan C., Miano, Todd A., Roche, Meaghan S., Vasquez, Charles R., Bansal, Amar D., Ernecoff, Natalie C., Kapoor, Sanjana, Verma, Siddharth, Chen, Huiwen, Kovesdy, Csaba P., Molnar, Miklos Z., Azhar, Ambreen, Hedayati, S. Susan, Nadamuni, Mridula V., Shastri, Shani, Willett, Duwayne L., Short, Samuel A.P., Renaghan, Amanda D., Enfield, Kyle B., Bhatraju, Pavan K., Malik, A. Bilal, Semler, Matthew W., Vijayan, Anitha, Joy, Christina Mariyam, Li, Tingting, Goldberg, Seth, Kao, Patricia F., Schumaker, Greg L., Christov, Marta, Griffiths, Jennifer, Gupta, Sanjeev, Kapoor, Aromma, Chugh, Savneek, Wilson, Perry, Arora, Tanima, Ugwuowo, Ugochukwu, Hsu, Caroline M., Gupta, Shruti, Tighiouart, Hocine, Goyal, Nitender, Faugno, Anthony J., Tariq, Asma, Raichoudhury, Ritesh, Sharma, Jill H., Meyer, Leah, Kshirsagar, Ravi K., Jose, Aju, Leaf, David E., and Weiner, Daniel E.

Published
2022
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46. Modulation of the Association Between Age and Death by Risk Factor Burden in Critically Ill Patients With COVID-19

Author

Sunderraj, Ashwin, Cho, Chloe, Cai, Xuan, Gupta, Shruti, Mehta, Rupal, Isakova, Tamara, Leaf, David E., Srivastava, Anand, Walther, Carl P., Anumudu, Samaya J., Arunthamakun, Justin, Kopecky, Kathleen F., Milligan, Gregory P., McCullough, Peter A., Nguyen, Thuy-Duyen, Shaefi, Shahzad, Krajewski, Megan L., Shankar, Sidharth, Pannu, Ameeka, Valencia, Juan D., Waikar, Sushrut S., Kibbelaar, Zoe A., Athavale, Ambarish M., Hart, Peter, Upadhyay, Shristi, Vohra, Ishaan, Oyintayo, Ajiboye, Green, Adam, Rachoin, Jean-Sebastien, Schorr, Christa A., Shea, Lisa, Edmonston, Daniel L., Mosher, Christopher L., Shehata, Alexandre M., Cohen, Zaza, Allusson, Valerie, Bambrick-Santoyo, Gabriela, Bhatti, Noor ul aain, Mehta, Bijal, Williams, Aquino, Brenner, Samantha K., Walters, Patricia, Go, Ronaldo C., Rose, Keith M., Hernán, Miguel A., Zhou, Amy M., Kim, Ethan C., Lisk, Rebecca, Chan, Lili, Mathews, Kusum S., Coca, Steven G., Altman, Deena R., Saha, Aparna, Soh, Howard, Hsun Wen, Huei, Bose, Sonali, Leven, Emily A., Wang, Jing G., Mosoyan, Gohar, Nadkarni, Girish N., Pattharanitima, Pattharawin, Gallagher, Emily J., Friedman, Allon N., Guirguis, John, Kapoor, Rajat, Meshberger, Christopher, Kelly, Katherine J., Parikh, Chirag R., Garibaldi, Brian T., Corona-Villalobos, Celia P., Wen, Yumeng, Menez, Steven, Malik, Rubab F., Elena Cervantes, Carmen, Gautam, Samir C., Mallappallil, Mary C., Ouyang, Jie, John, Sabu, Yap, Ernie, Melaku, Yohannes, Mohamed, Ibrahim, Bajracharya, Siddhartha, Puri, Isha, Thaxton, Mariah, Bhattacharya, Jyotsna, Wagner, John, Boudourakis, Leon, Bryant Nguyen, H., Ahoubim, Afshin, Thomas, Leslie F., Sirganagari, Dheeraj Reddy, Guru, Pramod K., Kashani, Kianoush, Tehranian, Shahrzad, Zhou, Yan, Bergl, Paul A., Rodriguez, Jesus, Shah, Jatan A., Gupta, Mrigank S., Kumar, Princy N., Lazarous, Deepa G., Kassaye, Seble G., Melamed, Michal L., Johns, Tanya S., Mocerino, Ryan, Prudhvi, Kalyan, Zhu, Denzel, Levy, Rebecca V., Azzi, Yorg, Fisher, Molly, Yunes, Milagros, Sedaliu, Kaltrina, Golestaneh, Ladan, Brogan, Maureen, Kumar, Neelja, Chang, Michael, Thakkar, Jyotsana, Raichoudhury, Ritesh, Athreya, Akshay, Farag, Mohamed, Schenck, Edward J., Cho, Soo Jung, Plataki, Maria, Alvarez-Mulett, Sergio L., Gomez-Escobar, Luis G., Pan, Di, Lee, Stefi, Krishnan, Jamuna, Whalen, William, Charytan, David, Macina, Ashley, Chaudhry, Sobaata, Wu, Benjamin, Modersitzki, Frank, Srivastava, Anand, Leidner, Alexander S., Martinez, Carlos, Kruser, Jacqueline M., Wunderink, Richard G., Hodakowski, Alexander J., Velez, Juan Carlos Q., Price-Haywood, Eboni G., Matute-Trochez, Luis A., Hasty, Anna E., Mohamed, Muner MB., Avasare, Rupali S., Zonies, David, Leaf, David E., Gupta, Shruti, Sise, Meghan E., Newman, Erik T., Abu Omar, Samah, Pokharel, Kapil K., Sharma, Shreyak, Singh, Harkarandeep, Correa, Simon, Shaukat, Tanveer, Kamal, Omer, Wang, Wei, Yang, Heather, Boateng, Jeffery O., Lee, Meghan, Strohbehn, Ian A., Li, Jiahua, Mueller, Ariel L., Redfern, Roberta E., Cairl, Nicholas S., Naimy, Gabriel, Abu-Saif, Abeer, Hall, Danyell, Bickley, Laura, Rowan, Chris, Madhani-Lovely, Farah, Cruz, Vivian S., Hess, Kristen M., Jacobs, Alanna L., Peev, Vasil, Reiser, Jochen, Byun, John J., Vissing, Andrew, Kapania, Esha M., Post, Zoe, Patel, Nilam P., Hermes, Joy-Marie, Sutherland, Anne K., Patrawalla, Amee, Finkel, Diana G., Danek, Barbara A., Arikapudi, Sowminya, Paer, Jeffrey M., Cangialosi, Peter, Liotta, Mark, Radbel, Jared, Puri, Sonika, Sunderram, Jag, Scharf, Matthew T., Ahmed, Ayesha, Berim, Ilya, Vatson, Jayanth S., Anand, Shuchi, Levitt, Joseph E., Garcia, Pablo, Boyle, Suzanne M., Song, Rui, Zhang, Jingjing, Hoon Woo, Sang, Deng, Xiaoying, Katz-Greenberg, Goni, Senter, Katharine, Sharshir, Moh’d A., Rusnak, Vadym V., Ali, Muhammad Imran, Bansal, Anip, Podoll, Amber S., Chonchol, Michel, Sharma, Sunita, Burnham, Ellen L., Douin, David J., Rashidi, Arash, Hejal, Rana, Judd, Eric, Latta, Laura, Tolwani, Ashita, Albertson, Timothy E., Adams, Jason Y., Chang, Steven Y., Beutler, Rebecca M., Schulze, Carl E., Macedo, Etienne, Rhee, Harin, Liu, Kathleen D., Jotwani, Vasantha K., Koyner, Jay L., Shah, Chintan V., Jaikaransingh, Vishal, Toth-Manikowski, Stephanie M., Joo, Min J., Lash, James P., Neyra, Javier A., Chaaban, Nourhan, Elias, Madona, Ahmad, Yahya, Iardino, Alfredo, Au, Elizabeth H., Sharma, Jill H., Anne Sosa, Marie, Taldone, Sabrina, Contreras, Gabriel, De La Zerda, David, Gershengorn, Hayley B., Shukla, Bhavarth, Fornoni, Alessia, Ferreira, Tanira, Hayek, Salim S., Blakely, Pennelope, Berlin, Hanna, Azam, Tariq U., Shadid, Husam, Pan, Michael, O’ Hayer, Patrick, Meloche, Chelsea, Feroze, Rafey, Kaakati, Rayan, Perry, Danny, Bitar, Abbas, Anderson, Elizabeth, Padalia, Kishan J., Donnelly, John P., Admon, Andrew J., Flythe, Jennifer E., Tugman, Matthew J., Chang, Emily H., Brown, Brent R., Leonberg-Yoo, Amanda K., Spiardi, Ryan C., Miano, Todd A., Roche, Meaghan S., Vasquez, Charles R., Bansal, Amar D., Ernecoff, Natalie C., Kapoor, Sanjana, Verma, Siddharth, Chen, Huiwen, Kovesdy, Csaba P., Molnar, Miklos Z., Azhar, Ambreen, Susan Hedayati, S., Nadamuni, Mridula V., Shastri, Shani, Willett, Duwayne L., Short, Samuel A.P., Renaghan, Amanda D., Enfield, Kyle B., Bhatraju, Pavan K., Bilal Malik, A., Semler, Matthew W., Vijayan, Anitha, Mariyam Joy, Christina, Li, Tingting, Goldberg, Seth, Kao, Patricia F., Schumaker, Greg L., Goyal, Nitender, Faugno, Anthony J., Schumaker, Greg L., Hsu, Caroline M., Tariq, Asma, Meyer, Leah, Kshirsagar, Ravi K., Weiner, Daniel E., Jose, Aju, Christov, Marta, Griffiths, Jennifer, Gupta, Sanjeev, Kapoor, Aromma, Wilson, Perry, Arora, Tanima, and Ugwuowo, Ugochukwu

Published
2022
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47. Pedestrian trajectory prediction method based on the Social-LSTM model for vehicle collision.

Author

Han, Yong, Lin, Xujie, Pan, Di, Li, Yanting, Su, Liang, Thomson, Robert, and Mizuno, Koji

Abstract

Techniques for predicting the trajectory of vulnerable road users are important to the development of perception systems for autonomous vehicles to avoid accidents. The most effective trajectory prediction methods, such as Social-LSTM, are often used to predict pedestrian trajectories in normal passage scenarios. However, they can produce unsatisfactory prediction results and data redundancy, as well as difficulties in predicting trajectories using pixel-based coordinate systems in collision avoidance systems. There is also a lack of validations using real vehicle-to-pedestrian collisions. To address these issues, some insightful approaches to improve the trajectory prediction scheme of Social-LSTM were proposed, such methods included transforming pedestrian trajectory coordinates and converting image coordinates to world coordinates. The YOLOv5 detection model was introduced to reduce target loss and improve prediction accuracy. The DeepSORT algorithm was employed to reduce the number of target transformations in the tracking model. Image Perspective Transformation (IPT) and Direct Linear Transformation (DLT) theories were combined to transform the coordinates to world coordinates, identifying the collision location where the accident could occur. The performance of the proposed method was validated by training tests using MS COCO (Microsoft Common Objects in Context) and ETH/UCY datasets. The results showed that the target detection accuracy was more than 90% and the prediction loss tends to decrease with increasing training steps, with the final loss value less than 1%. The reliability and effectiveness of the improved method were demonstrated by benchmarking system performance to two video recordings of real pedestrian accidents with different lighting conditions. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Influences of thermal crown and wear crown of work roll on strip shape in tandem cold rolling using a novel 3D multi-pass FE model.

Author

Li, Lianjie, Xie, Haibo, Zhang, Tao, Pan, Di, Liu, Tianwu, Li, Xingsheng, Liu, Xu, Wang, Enrui, Liu, Hongqiang, Sun, Li, and Jiang, Zhengyi

Abstract

Thermal crown and wear crown of work roll (TWW) are the main interfering factors of loaded roll gap profile in tandem cold rolling (TCR). However, the effect of TWW on the strip shape is not well understood. This paper presents a quantitative study about the effect of TWW on the strip crown and strip flatness based on a novel 3D multi-pass elastic-plastic finite element (EPFE) model that has been validated by industrial trials in the TCR. The results show that the thermal crown introduces the centre wave and quarter wave, while the wear crown brings in the edge wave and edge-centre coupled wave; the thermal crown has a larger influence efficiency on the quadratic strip shape than the wear crown does, while the wear crown exerts a larger influence efficiency on the quartic strip shape than the thermal crown does. In addition, the influence efficiency of TWW on the strip crown decreases nonlinearly with an increase in strip plastic rigidity from Pass 1 (P1) to Pass 5 (P5). This is the first scientific report on the link between the strip plastic rigidity and the effect of TWW on the strip crown, affording the mathematical models for predicting the influence efficiency of TWW based on the strip plastic rigidity at each pass. [ABSTRACT FROM AUTHOR]

Published
2024
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