{"title":"Wireless Real-Time Exercise System for Physical Telerehabilation","authors":"Aref Smiley, Te-Yi Tsai, J. Finkelstein","doi":"10.1109/LISAT50122.2022.9923944","DOIUrl":"https://doi.org/10.1109/LISAT50122.2022.9923944","url":null,"abstract":"The COVID-19 pandemic has impacted every aspects of health delivery and encouraged researchers to replace in-person clinical visits with telecommunications. Such approach has great potential to support home-based exercises. A critical part of successful use of exercise equipment at patient homes is to account for potential patient’s needs, values and preferences for rehabilitation and exercise prescriptions. The goal if this pilot project is to conduct an initial usability assessment of a remotely controlled version of interactive bike (iBike) system which gives the clinical team the capability to monitor exercise progress in real time using simple graphical representation. The bike can be used for upper or lower limb rehabilitation. A customized tablet app was developed to provide user interface between the app and the bike sensors. The iBike system was tested using a quasi-experimental design based on pre-post single group comparison. Two separate sessions, with a gap of a week and no further training and use of iBikE system, were carried out to determine the usability of the iBikE system. The completion times of Task 1 and Task 2 changed from 8.6±4.7 seconds to 1.8±0.8 seconds and from 315.0±6.9 seconds to 303.4±1.1 seconds respectively. Between pre-post tests, the 3-item post-task survey scores increased and the usability scores increased from 92.0±8.6 to 97.0±3.3. From the question of “How likely are you to recommend this iBikE system to others?” the scores increased from 9.4±0.9 to 9.8±0.5. We concluded that further development and evaluation of the iBike system in different patient subgroups is warranted.","PeriodicalId":380048,"journal":{"name":"2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128915413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Lifelong Learning Repetitive Neuro-Controller","authors":"A. Chinnan, Tina Chinnan","doi":"10.1109/LISAT50122.2022.9924004","DOIUrl":"https://doi.org/10.1109/LISAT50122.2022.9924004","url":null,"abstract":"Over the past five years, almost every application of engineering has tried to implement an artificial neural network to improve performance in one way or another. The study of real neurons in the human brain on the other hand, which paved the way for such applications, has been ongoing for well over five decades. While significant progress has been made, artificial neurons fall well short of the capabilities of real neurons in some key areas, such as the ability to perform lifelong learning. This limitation can be traced back to fundamental differences in network architecture and overall implementation. To remedy this, attention must first be directed back to the foundational science behind how the human brain acquires, stores, uses, and selectively removes specific memory or knowledge. Next, novel architectural concepts and implementation strategies to address the aforementioned limitations must be developed. Here, this will be done through careful considerations of all aspects within the repetitive regime. The human brain, by design, is very reliant on continual lifelong learning to solve problems. Control strategies used today, artificial neural networks, and even combinations of both are unable to optimally engage in this fundamental process. This paper attempts to bridge the gap and push toward enhanced lifelong learning control strategies for future use.","PeriodicalId":380048,"journal":{"name":"2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127036856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Mismatch Accumulation Effects of Cascaded Switches for Multi-Bit Delay Path Selection of Phased Array Beam Steering","authors":"P.E. Lawrence Hausman","doi":"10.1109/LISAT50122.2022.9924094","DOIUrl":"https://doi.org/10.1109/LISAT50122.2022.9924094","url":null,"abstract":"Phased array radars require beam boresights that are capable of fast switching between different azimuths and elevations, typically in two dimensions simultaneously. Narrowband radar can use phase shifter components to achieve phase change, which translates to time delay for very narrow band signals. A radar having an appreciable bandwidth, such as a chirped radar, requires selection of variable true signal time delay with different time delays spatially distributed over the face of the radar in order to enable the spatial combining of the transmitted or received signal required to achieve beam steering. These electrically adjustable true time delay devices, or Time Delay Units (TDUs), are constructed from cascades of radio frequency (RF) signal switches typically configured into groups of two switches, referred to as bits. Each bit can then, by operation of its electrical switches, select one signal propagation path or a second propagation path, where the second path is a predetermined electrical length longer than the first path. In construction of this cascaded switch arrangement, mismatch errors can occur at each interface between switch elements and between a switch element and a delay line, due to the non-ideal nature of electrical semiconductors used for enabling the electrical switching function. In order to effectively bound the cumulative effect of these mismatch errors when taken over the entire cascaded switching structure, an understanding of the composition of each error is necessary. Also useful is a method for bounding the cumulative effects of the cascaded network. Components of an error vector are examined using the phasor domain. Choice of statistical methods to bound the cascaded network are also discussed.","PeriodicalId":380048,"journal":{"name":"2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125397314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Shiny Dashboard - NYC Trees Benefit Estimation","authors":"Elona Zharri, S. Robila","doi":"10.1109/LISAT50122.2022.9923953","DOIUrl":"https://doi.org/10.1109/LISAT50122.2022.9923953","url":null,"abstract":"This project describes the research and development efforts to develop a web-based interface that allows the visualization of how the tree canopy confers ecological, economic, and social benefits and how it can be used to enhance neighborhoods disproportionately affected by environmental challenges such as excessive heat, impervious surfaces, and air pollution. It provides an example how data science can provide communities with tools that employ Open Data to research impact of vegetation spread to aspects such as energy usage, house prices and new planting. Using the analysis and visualization of the NYC trees dataset, an interactive Shiny dashboard representing 2015 New York City Street Tree Census - Tree Data was developed. The data was curated into an easier form to understand, highlighted the trends and outliers as a pictorial and graphical format to illustrate the existing trees planted around a given address, and estimation benefit of the trees based on their age, condition, and type. The application development provides an example how data science can provide communities with tools that employ Open Data to research impact of vegetation spread to aspects such as energy usage or house prices.","PeriodicalId":380048,"journal":{"name":"2022 IEEE Long Island Systems, Applications and Technology Conference (LISAT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129027250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}