In many modern enterprises, factory managers monitor their machinery and processes to prevent faults and product defects, and maximize the productivity and efficiency. Asset condition, product quality and system productivity monitoring consume some 40-70% of the production costs. Oftentimes, resource constraints have prevented the adoption and implementation of these practices in small businesses. Recent evolution of manufacturing-as-a-service and increased digitalization opens opportunities for small and medium scale companies to adopt smart manufacturing practices, and thereby surmount these constraints. Specifically, sensor wrappers that delineate the specifications of sensor integration into manufacturing machinery, with appropriate edge-cloud computing and communication architecture can provide even small businesses with a
real-time data pipeline to monitor their manufacturing machines. However, the data in itself is difficult to interpret locally. Additionally, proprietary standards and products of the various components of a sensor wrapper make it difficult to implement a sensor wrapper schema. In this paper, we report an open-source method to integrate sensors into legacy manufacturing equipment and hardware. We had implemented this pipeline with off-the-shelf sensors to a polisher (from Buehler), a shaft grinding machine (from Micromatic), and a hybrid manufacturing machine (from Optomec), and used hardware and software components such as a National Instruments Data Acquisition (NI-DAQ) module to collect and stream live data. We evaluate the performance of the data pipeline as it connects to the Smart Manufacturing Innovation Platform (SMIP)—web-based data ingestion platform part of the Clean Energy Smart Manufacturing Innovation Institute (CESMII), a U.S. Department of Energy-sponsored initiative—in terms of data volume versus latency tradeoffs. We demonstrate a viable implementation of Smart Manufacturing by creating a vendor-agnostic web dashboard that fuses multiple sensors to perform real-time performance analysis with lossless data integrity.
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Respiration Monitoring using a Motion Tape Chest Band and Portable Wireless Sensing Node
The measurement of vital signs (such as respiration rate, body temperature, pulse, and blood pressure), especially during strenuous activities, is essential for physical performance and health monitoring. A variety of wearable chest band sensors have been developed, commercialized, and widely used in consumer and healthcare settings. The plethora of technology choices also means that each unique chest band sensor may require different data acquisition hardware and software systems, and data may not be transferable between platforms. Therefore, the objective of this work was to develop a low-cost, disposable, respiration sensor that could be attached onto any elastic chest band. The approach was to spray-coat graphene nanosheet (GNS)-based thin films onto unidirectionally stretchable elastic fabric to form a piezoresistive material. Snap buttons were incorporated at the ends of the fabric so that they could be attached onto any chest band, removed at any time, and replaced for a new data collection event. The resistive nature of the nanocomposite sensor means that they can be easily interfaced (e.g., using a voltage divider) with any existing data acquisition (DAQ) module while adding respiration monitoring capabilities. To facilitate testing of these nanocomposite respiration sensors, a miniature DAQ module with four sensing channels was also prototyped. Then, tests were performed with human subjects wearing a nanocomposite chest band and a reference commercial respiration monitoring chest band. Simultaneous measurements of subject respiration verified the respiration monitoring performance of these low-cost, disposable, nanocomposite fabric sensors.
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- Award ID(s):
- 1757994
- NSF-PAR ID:
- 10337869
- Date Published:
- Journal Name:
- Journal of Commercial Biotechnology
- Volume:
- 27
- Issue:
- 1
- ISSN:
- 1462-8732
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Proc. ACM Interact. Mob. Wearable Ubiquitous Technol .,3 , 1–29 (2019)). However, many sensing scenarios, such as sleep and posture monitoring, involve an added static pressure from exerted body weight, which overpowers weaker pressure signals originating from heartbeats, respiration and pulse and phonation. Here, we introduce an all-fabric piezoionic pressure sensor (PressION) that, on account of its ionic conductivity, functions over a wide range of static and dynamic applied pressures (from subtle ballistic heartbeats and pulse waveforms, to larger-scale body movements). This piezoionic sensor also maintains its pressure responsivity in the presence of an added background pressure and upon integration into loose-fitting garments. The broad ability of PressION to record a wide variety of physiological signals in realistic environments was confirmed by acquiring heartbeat, pulse, joint motion, phonation and step data from different body locations. PressION’s sensitivity, along with its low-cost fabrication process, qualifies it as a uniquely useful sensing element in wearable health monitoring systems. -
An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer WorkSeveral unobtrusive sensors have been tested in studies to capture physiological reactions to stress in workplace settings. Lab studies tend to focus on assessing sensors during a specific computer task, while in situ studies tend to offer a generalized view of sensors’ efficacy for workplace stress monitoring, without discriminating different tasks. Given the variation in workplace computer activities, this study investigates the efficacy of unobtrusive sensors for stress measurement across a variety of tasks. We present a comparison of five physiological measurements obtained in a lab experiment, where participants completed six different computer tasks, while we measured their stress levels using a chest-band (ECG, respiration), a wristband (PPG and EDA), and an emerging thermal imaging method (perinasal perspiration). We found that thermal imaging can detect increased stress for most participants across all tasks, while wrist and chest sensors were less generalizable across tasks and participants. We summarize the costs and benefits of each sensor stream, and show how some computer use scenarios present usability and reliability challenges for stress monitoring with certain physiological sensors. We provide recommendations for researchers and system builders for measuring stress with physiological sensors during workplace computer use.more » « less
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Abstract Wearable electromechanical sensors are essential to improve health monitoring and off‐site point‐of‐care applications. However, their practicality is restricted by narrow ranges of detection, failure to simultaneously sense static and dynamic pressures, and low durability. Here, an all‐fabric pressure sensor with high sensitivity in a broad range of pressures, from subtle heart pulses to body posture, exceeding that of previously‐reported sensors is introduced. By taking advantage of chemical vapor deposition of p‐doped poly(3,4‐ethylenedioxythiophene) chloride (PEDOT‐Cl) on two natural textiles (cotton gauze and cotton balls), multiscale tunable pressure sensitivity with low power demand for data read‐out is obtained. To protect the sensor against humidity induced degradations, the sensor is encapsulated with a hydrophobic coating that leads to ultrastability of the sensor performance even after 1 week of exposure to 100% relative humidity and 20 laundry cycles. The sensor reveals excellent performance retention of >99% over 70 000 bending cycles under ambient conditions. The varied utility of this sensor for health monitoring is demonstrated by recording heartbeats, respiration, and joint movements. Furthermore, using this sensor, grip strength is successfully detected by 93.6% accuracy as compared to commercial dynamometer, speaking of its potential as the first fabric‐based sensor allowing for personalized real‐time grip strength analysis.
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5912/jcb1026
