Recent advancements in sensors, device manufacturing, and big data technologies have enabled the design and manufacturing of smart wearables for a wide array of applications in healthcare. These devices can be used to remotely monitor and diagnose various diseases and aid in the rehabilitation of patients. Smart wearables are an unobtrusive and affordable alternative to costly and time-consuming health care efforts such as hospitalization and late diagnosis. Developments in micro- and nanotechnologies have led to the miniaturization of sensors, hybrid 3D printing of flexible plastics, embedded electronics, and intelligent fabrics, as well as wireless communication mediums that permit the processing, storage, and communication of data between patients and healthcare facilities. Due to these complex component architectures that comprise smart wearables, manufacturers have faced a number of problems, including minimum sensor configuration, data security, battery life, appropriate user interfaces, user acceptance, proper diagnosis, and many more. There has been a significant increase in interest from both the academic and industrial communities in research and innovation related to smart wearables. However, since smart wearables integrate several different aspects such as design, manufacturing, and analytics, the existing literature is quite widespread, making it less accessible for researchers and practitioners. The purpose of this study is to narrow this gap by providing a state-of-the-art review of the extant design, manufacturing, and analytics literature on smart wearables-all in one place- thereby facilitating future work in this rapidly growing field of research and application. Lastly, it also provides an in-depth discussion on two very important challenges facing the smart wearable devices, which include barriers to user adoption and the manufacturing technologies of the wearable devices.
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This content will become publicly available on June 7, 2024
Smart Pill Dispenser with Smart Cup
This research paper describes the design of a pill dispensing device that can assist people with physical or cognitive limitations in taking their prescribed medications. The design is based on the communication between two devices for the purpose of dispensing pills at a scheduled time and identifying if these pills had been properly consumed within a specified time frame. The two devices are based on Arduino RP2040 connect microcontrollers and implement several sensors in the aid of dispensing and detecting of pill consumption. The sensors implemented are an IMU, and distances sensors, such as an ultrasonic sensor and an IR proximity sensor, additionally a real time clock module and stepper motor have been included in the design for the scheduling and dispensing of the pills. The two devices will communicate using Bluetooth for low energy devices (BLE) and the purpose of the devices is to provide aid to the intended target audience in achieving a healthier lifestyle.
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- Award ID(s):
- 2125654
- NSF-PAR ID:
- 10466543
- Publisher / Repository:
- IEEE
- Date Published:
- Page Range / eLocation ID:
- 0598 to 0604
- Subject(s) / Keyword(s):
- ["Bluetooth BLE","Arduino Connect","Stepper Motor","Assistive Technology","IMU","IR proximity","Ultrasonic"]
- Format(s):
- Medium: X
- Location:
- Seattle, WA, USA
- Sponsoring Org:
- National Science Foundation
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Abstract Recent advancements in sensors, device manufacturing and big data technologies have enabled the design and manufacturing of smart wearables for a wide array of applications in health care. These devices can be used to remotely monitor and diagnose various diseases and aid in the rehabilitation of patients. Smart wearables are an unobtrusive and affordable alternative to costly and time‐consuming healthcare efforts such as hospitalization and late diagnosis. Developments in micro‐ and nanotechnologies have led to the miniaturization of sensors, hybrid 3D printing of flexible plastics, embedded electronics and intelligent fabrics, as well as wireless communication mediums that permit the processing, storage and communication of data between patients and healthcare facilities. Due to these complex component architectures that comprise smart wearables, manufacturers have faced a number of problems, including minimum sensor configuration, data security, battery life, appropriate user interfaces, user acceptance, proper diagnosis and many more. There has been a significant increase in interest from both the academic and industrial communities in research and innovation related to smart wearables. However, as smart wearables integrate several different aspects such as design, manufacturing and analytics, the existing literature is quite widespread, making it less accessible for researchers and practitioners. The purpose of this study was to narrow this gap by providing a state‐of‐the‐art review of the extant design, manufacturing and analytics literature on smart wearables—all in one place—thereby facilitating future work in this rapidly growing field of research and application. Lastly, it also provides an in‐depth discussion on two very important challenges facing the smart wearable devices, which include barriers to user adoption and the manufacturing technologies of the wearable devices.
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This research paper describes the design of a device that can assist seniors or people with physical or cognitive limitations to take their prescribed medications that are in the form of pills on time while verifying that such pills have been actually consumed. The design consists of a portable smart pill dispenser that will rest on a base, allowing it to dispense pills into a smart cup. The smart pill dispenser uses a stepper motor to rotate to a desired pills based on a specific time slot/day of the week. The smart cup attached to the pill box uses an accelerometer, gyroscope, and an IR proximity sensor to detect if a user is taking the medication by how much the smart cup is lifted and tilted. The smart cup will inform the smart pill dispenser if the pills are properly consumed or not, thus, allowing the device to potentially aid the patients to have a healthier life.more » « less
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Raynal, Ann M. ; Ranney, Kenneth I. (Ed.)Most research in technologies for the Deaf community have focused on translation using either video or wearable devices. Sensor-augmented gloves have been reported to yield higher gesture recognition rates than camera-based systems; however, they cannot capture information expressed through head and body movement. Gloves are also intrusive and inhibit users in their pursuit of normal daily life, while cameras can raise concerns over privacy and are ineffective in the dark. In contrast, RF sensors are non-contact, non-invasive and do not reveal private information even if hacked. Although RF sensors are unable to measure facial expressions or hand shapes, which would be required for complete translation, this paper aims to exploit near real-time ASL recognition using RF sensors for the design of smart Deaf spaces. In this way, we hope to enable the Deaf community to benefit from advances in technologies that could generate tangible improvements in their quality of life. More specifically, this paper investigates near real-time implementation of machine learning and deep learning architectures for the purpose of sequential ASL signing recognition. We utilize a 60 GHz RF sensor which transmits a frequency modulation continuous wave (FMWC waveform). RF sensors can acquire a unique source of information that is inaccessible to optical or wearable devices: namely, a visual representation of the kinematic patterns of motion via the micro-Doppler signature. Micro-Doppler refers to frequency modulations that appear about the central Doppler shift, which are caused by rotational or vibrational motions that deviate from principle translational motion. In prior work, we showed that fractal complexity computed from RF data could be used to discriminate signing from daily activities and that RF data could reveal linguistic properties, such as coarticulation. We have also shown that machine learning can be used to discriminate with 99% accuracy the signing of native Deaf ASL users from that of copysigning (or imitation signing) by hearing individuals. Therefore, imitation signing data is not effective for directly training deep models. But, adversarial learning can be used to transform imitation signing to resemble native signing, or, alternatively, physics-aware generative models can be used to synthesize ASL micro-Doppler signatures for training deep neural networks. With such approaches, we have achieved over 90% recognition accuracy of 20 ASL signs. In natural environments, however, near real-time implementations of classification algorithms are required, as well as an ability to process data streams in a continuous and sequential fashion. In this work, we focus on extensions of our prior work towards this aim, and compare the efficacy of various approaches for embedding deep neural networks (DNNs) on platforms such as a Raspberry Pi or Jetson board. We examine methods for optimizing the size and computational complexity of DNNs for embedded micro-Doppler analysis, methods for network compression, and their resulting sequential ASL recognition performance.more » « less
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Abstract The demand for the capacitive sensor has attracted substantial attention in monitoring pressure due to its distinctive design and passive nature with versatile sensing capability. The effectiveness of the capacitive sensor primarily relies on the variation in thickness of the dielectric layer sandwiched between conductive electrodes. Additive manufacturing (AM), a set of advanced fabrication techniques, enables the production of functional electronic devices in a single-step process. Particularly, the 3D printing approach based on photocuring is a tailorable process in which the resin consists of multiple components that deliver essential mechanical qualities with enhanced sensitivity towards targeted measurements. However, the availability of photocurable resin exhibiting essential flexibility and dielectric properties for the UV-curing production process is limited. The necessity of a highly stable and sensitive capacitive sensor demands a photocurable polymer resin with a higher dielectric constant and conductive electrodes. The primary purpose of this study is to design and fabricate a capacitive device composed of novel photocurable Polyvinylidene fluoride (PVDF) resin utilizing an LCD process exhibiting higher resolution with electrodes embedded inside the substrate. The embedded electrode channels in PVDF substrate are filled with conductive silver paste by an injection process. The additively manufactured sensor provides pressure information by means of a change in capacitance of the dielectric material between the electrodes. X-Ray based micro CT-Scan ex-situ analysis is performed to visualize the capacitance based sensor filled with conductive electrodes. The sensor is tested to measure capacitance response with changes in pressure as a function of time that are utilized for sensitivity analysis. This work represents a significant achievement of AM integration in developing efficient and robust capacitive sensors for pressure monitoring or wearable electronic applications.
