A Cost-Effective and Smart Sensing Tissue-like Testbed for Surgical Training

A low-cost tissue-like testbed with six nodes of varying stiffness was developed for surgical training to provide pressure and force feedback data through image reception to human operators. Using SolidWorks, a 3D model of the box trainer housing was created. A pad for the distribution of smartsensing nodes and microcontroller connections was designed with open spaces for the respective components. The pad was 3D-printed with PLA filament. Flat piezoelectric pressure sensors were fabricated with conductive materials and velostat sensor material. Using static and dynamic analyses, three top sensors were chosen to be used in three pressure sensing nodes. A calibration process was performed on the pressure sensors to find the linear relationship between voltage and pressure, which was then used to create a conversion equation for each sensor. These equations were used to collect data at the three pressure sensing nodes on the silicone testbed pad. Conductive TPU filament was used to 3D-print vertical force sensors, which were designed using SolidWorks. The force sensors were calibrated with a squeezing mechanism to find a relationship between voltage and force and to subsequently develop a conversion equation for each sensor. We used these equations to collect force data from the three force sensing nodes on the testbed pad. Through static and dynamic analyses, the force sensors were found to be functional, but to need improvements in accuracy. The mechatronic system was designed and developed to integrate all six sensors and to collect data from the testbed pad using an Arduino microcontroller. The flat pressure and vertical force sensors were embedded in each node to measure the pressure and force that occurs during the deformation of the six nodes. Data was collected and imported into MATLAB. This data was used in displaying pressure and force mapping of the nodes over a live video of the silicone pad. Pressure and force mapping was realized by drawing color-coded circles on each of the six nodes that correspond to a range of force or pressure values. From this development, the surgical testbed provides multi-stiffness tissue training with live pressure and force mapping overlaid on a live video of the emulated surgical field

Smart Lighting Clinical Testbed Pilot Study on Circadian Phase Advancement

Objective: Lighting is a strong synchronizer for circadian rhythms, which in turn drives a wide range of biological functions. The objective of our work is a) to construct a clinical in-patient testbed with smart lighting, and b) evaluate its feasibility for use in future clinical studies. Methods: A feedback capable, variable spectrum lighting system was installed at the University of New Mexico Hospital. The system consists of variable spectrum lighting troffers, color sensors, occupancy sensors, and computing and communication infrastructure. We conducted a pilot study to demonstrate proof of principle, that 1) this new technology is capable of providing continuous lighting and sensing in an active clinical environment, 2) subject recruitment and retention is feasible for round-the-clock, multi-day studies, and 3) current techniques for circadian regulation can be deployed in this unique testbed. Unlike light box studies, only troffer-based lighting was used, and both lighting intensity and spectral content were varied. Results: The hardware and software functioned seamlessly to gather biometric data and provide the desired lighting. Salivary samples that measure dim-light melatonin onset showed phase advancement for all three subjects. Conclusion: We executed a five-day circadian rhythm study that varied intensity, spectrum, and timing of lighting as proof-of-concept or future clinical studies with troffer-based, variable spectrum lighting. Clinical Impact: The ability to perform circadian rhythm experiments in more realistic environments that do not overly constrain the subject is important for translating lighting research into practice, as well as for further research on the health impacts of lighting

Jefferson Digital Commons quarterly report: January-March 2020

This quarterly report includes: New Look for the Jefferson Digital Commons Articles COVID-19 Working Papers Educational Materials From the Archives Grand Rounds and Lectures JeffMD Scholarly Inquiry Abstracts Journals and Newsletters Master of Public Health Capstones Oral Histories Posters and Conference Presentations What People are Saying About the Jefferson the Digital Common

Discovering user mobility and activity in smart lighting environments

"Smart lighting" environments seek to improve energy efficiency, human productivity and health by combining sensors, controls, and Internet-enabled lights with emerging “Internet-of-Things” technology. Interesting and potentially impactful applications involve adaptive lighting that responds to individual occupants' location, mobility and activity. In this dissertation, we focus on the recognition of user mobility and activity using sensing modalities and analytical techniques. This dissertation encompasses prior work using body-worn inertial sensors in one study, followed by smart-lighting inspired infrastructure sensors deployed with lights. The first approach employs wearable inertial sensors and body area networks that monitor human activities with a user's smart devices. Real-time algorithms are developed to (1) estimate angles of excess forward lean to prevent risk of falls, (2) identify functional activities, including postures, locomotion, and transitions, and (3) capture gait parameters. Two human activity datasets are collected from 10 healthy young adults and 297 elder subjects, respectively, for laboratory validation and real-world evaluation. Results show that these algorithms can identify all functional activities accurately with a sensitivity of 98.96% on the 10-subject dataset, and can detect walking activities and gait parameters consistently with high test-retest reliability (p-value < 0.001) on the 297-subject dataset. The second approach leverages pervasive "smart lighting" infrastructure to track human location and predict activities. A use case oriented design methodology is considered to guide the design of sensor operation parameters for localization performance metrics from a system perspective. Integrating a network of low-resolution time-of-flight sensors in ceiling fixtures, a recursive 3D location estimation formulation is established that links a physical indoor space to an analytical simulation framework. Based on indoor location information, a label-free clustering-based method is developed to learn user behaviors and activity patterns. Location datasets are collected when users are performing unconstrained and uninstructed activities in the smart lighting testbed under different layout configurations. Results show that the activity recognition performance measured in terms of CCR ranges from approximately 90% to 100% throughout a wide range of spatio-temporal resolutions on these location datasets, insensitive to the reconfiguration of environment layout and the presence of multiple users.2017-02-17T00:00:00

Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment

[EN] Aging population increase demands for solutions to help the solo-resident elderly live independently. Unobtrusive data collection in a smart home environment can monitor and assess elderly residents' health state based on changes in their mobility patterns. In this paper, a smart home system testbed setup for a solo-resident house is discussed and evaluated. We use paired Passive infra-red (PIR) sensors at each entry of a house and capture the resident's activities to model mobility patterns. We present the required testbed implementation phases, i.e., deployment, post-deployment analysis, re-deployment, and conduct behavioural data analysis to highlight the usability of collected data from a smart home. The main contribution of this work is to apply intelligence from a post-deployment process mining technique (namely, the parallel activity log inference algorithm (PALIA)) to find the best configuration for data collection in order to minimise the errors. Based on the post-deployment analysis, a re-deployment phase is performed, and results show the improvement of collected data accuracy in re-deployment phase from 81.57% to 95.53%. To complete our analysis, we apply the well-known CASAS project dataset as a reference to conduct a comparison with our collected results which shows a similar pattern. The collected data further is processed to use the level of activity of the solo-resident for a behaviour assessment.Shirali, M.; Bayo-Monton, JL.; Fernández Llatas, C.; Ghassemian, M.; Traver Salcedo, V. (2020). Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment. Sensors. 20(24):1-25. https://doi.org/10.3390/s20247167S1252024Lutz, W., Sanderson, W., & Scherbov, S. (2001). The end of world population growth. 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Journal of Network and Computer Applications, 97, 48-65. doi:10.1016/j.jnca.2017.08.017Palipana, S., Pietropaoli, B., & Pesch, D. (2017). Recent advances in RF-based passive device-free localisation for indoor applications. Ad Hoc Networks, 64, 80-98. doi:10.1016/j.adhoc.2017.06.007Chen, G., Wang, A., Zhao, S., Liu, L., & Chang, C.-Y. (2017). Latent feature learning for activity recognition using simple sensors in smart homes. Multimedia Tools and Applications, 77(12), 15201-15219. doi:10.1007/s11042-017-5100-4Tewell, J., O’Sullivan, D., Maiden, N., Lockerbie, J., & Stumpf, S. (2019). Monitoring meaningful activities using small low-cost devices in a smart home. Personal and Ubiquitous Computing, 23(2), 339-357. doi:10.1007/s00779-019-01223-2Krishnan, N. C., & Cook, D. J. (2014). Activity recognition on streaming sensor data. Pervasive and Mobile Computing, 10, 138-154. doi:10.1016/j.pmcj.2012.07.003Wang, A., Chen, G., Wu, X., Liu, L., An, N., & Chang, C.-Y. (2018). Towards Human Activity Recognition: A Hierarchical Feature Selection Framework. Sensors, 18(11), 3629. doi:10.3390/s18113629Liu, Y., Wang, X., Zhai, Z., Chen, R., Zhang, B., & Jiang, Y. (2019). Timely daily activity recognition from headmost sensor events. ISA Transactions, 94, 379-390. doi:10.1016/j.isatra.2019.04.026Viani, F., Robol, F., Polo, A., Rocca, P., Oliveri, G., & Massa, A. (2013). Wireless Architectures for Heterogeneous Sensing in Smart Home Applications: Concepts and Real Implementation. Proceedings of the IEEE, 101(11), 2381-2396. doi:10.1109/jproc.2013.2266858Rashidi, P., Cook, D. J., Holder, L. B., & Schmitter-Edgecombe, M. (2011). Discovering Activities to Recognize and Track in a Smart Environment. 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The Gator Tech Smart House: a programmable pervasive space. Computer, 38(3), 50-60. doi:10.1109/mc.2005.107Doctor, F., Hagras, H., & Callaghan, V. (2005). A Fuzzy Embedded Agent-Based Approach for Realizing Ambient Intelligence in Intelligent Inhabited Environments. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 35(1), 55-65. doi:10.1109/tsmca.2004.838488Abowd, G. D., & Mynatt, E. D. (2005). Designing for the Human Experience in Smart Environments. Smart Environments, 151-174. doi:10.1002/047168659x.ch7Technology Integrated Health Management (TIHM) Project https://www.sabp.nhs.uk/tihmAhvar, E., Daneshgar-Moghaddam, N., Ortiz, A. M., Lee, G. M., & Crespi, N. (2016). On analyzing user location discovery methods in smart homes: A taxonomy and survey. Journal of Network and Computer Applications, 76, 75-86. doi:10.1016/j.jnca.2016.09.012Milenkovic, M., & Amft, O. (2013). 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ANGELAH: A Framework for Assisting Elders At Home

The ever growing percentage of elderly people within modern societies poses welfare systems under relevant stress. In fact, partial and progressive loss of motor, sensorial, and/or cognitive skills renders elders unable to live autonomously, eventually leading to their hospitalization. This results in both relevant emotional and economic costs. Ubiquitous computing technologies can offer interesting opportunities for in-house safety and autonomy. However, existing systems partially address in-house safety requirements and typically focus on only elder monitoring and emergency detection. The paper presents ANGELAH, a middleware-level solution integrating both ”elder monitoring and emergency detection” solutions and networking solutions. ANGELAH has two main features: i) it enables efficient integration between a variety of sensors and actuators deployed at home for emergency detection and ii) provides a solid framework for creating and managing rescue teams composed of individuals willing to promptly assist elders in case of emergency situations. A prototype of ANGELAH, designed for a case study for helping elders with vision impairments, is developed and interesting results are obtained from both computer simulations and a real-network testbed

Digital-Twins towards Cyber-Physical Systems: A Brief Survey

Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Physical processes are monitored and controlled by embedded computers and networks, which frequently have feedback loops where physical processes affect computations and vice versa. To ease the analysis of a system, the costly physical plants can be replaced by the high-fidelity virtual models that provide a framework for Digital-Twins (DT). This paper aims to briefly review the state-of-the-art and recent developments in DT and CPS. Three main components in CPS, including communication, control, and computation, are reviewed. Besides, the main tools and methodologies required for implementing practical DT are discussed by following the main applications of DT in the fourth industrial revolution through aspects of smart manufacturing, sixth wireless generation (6G), health, production, energy, and so on. Finally, the main limitations and ideas for future remarks are talked about followed by a short guideline for real-world application of DT towards CPS

In-body Communications Exploiting Light:A Proof-of-concept Study Using ex vivo Tissue Samples

This article presents a feasibility study on the transmission of information through the biological tissues exploiting light. The experimental results demonstrating the potentials of optical wireless communications through biological tissues (OCBT) are presented. The main application of the proposed technology is in-body communications, where wireless connectivity needs to be provided to implanted electronic devices, such as pacemakers, cardiac defibrillators, and smart pills, for instance. Traditionally, in-body communications are performed using radio and acoustic waves. However, light has several fundamental advantages making the proposed technology highly attractive for this purpose. In particular, optical communications are highly secure, private, safe, and in many cases, extremely simple with the potential of low-power implementation. In the experiments, near-infrared light was used, as the light propagation in biotissues is more favorable in this part of the spectrum. The amount of light exposure given to biotissues was controlled to keep it within the safety limits. Information transmission experiments were carried out with the temperature-controlled ex vivo samples of porcine tissue. The tissue temperature was found to be significantly affecting the light propagation process. Communication performance with respect to the biotissue thickness and light direction was assessed. The results showed that optical channels to and from the possible implant are nearly reciprocal. Communication links were established to the deepness of more than four centimeters, and the data rates of up to 100 Kbps were obtained. The encouraging results of this study allow us to anticipate the potential applications of the proposed light-based technology to communicate with the various electronic devices implanted at different depths in the human body