Design of an intelligent medical splint with 3D printing and pressure detection

Li, Kun; Sun, Zhaolong; Liu, Junyou; Guo, Ai; Wang, Aiguo; Lü, Jun; Xue, Fu‐Shan

doi:10.1109/icbaie52039.2021.9389935

Cited by 4 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additional areas of research include medical sensor design [14][15][16][17][18], body area networks [19,20], and WBANs [21][22][23]. This paper's unique contribution lies in a comprehensive review of physical layer (PHY) technologies in WBANs, highlighting testbed implementations, and providing an end-to-end overview of M2M systems within mHealth contexts.…”

Section: M2m Technologies With Application To Bci and Eegmentioning

confidence: 99%

Energy-Efficient Deep Neural Networks for EEG Signal Noise Reduction in Next-Generation Green Wireless Networks and Industrial IoT Applications

Kumar,

Chakravarthy,

Nanthaamornphong

2023

Symmetry

View full text Add to dashboard Cite

Wireless electroencephalography (EEG) has emerged as a critical interface between human cognitive processes and machine learning technologies in the burgeoning field of sensor communications. This paper presents a comprehensive review of advancements in wireless EEG communication and analysis, with an emphasis on their role in next-generation green wireless networks and industrial IoT. The review explores the efficacy of modulation techniques, such as amplitude-shift keying (ASK) and frequency-shift keying (FSK) in EEG data transmission, and emphasizes the transformative role of deep learning in the joint transmission and restoration of EEG signals. In addition, we propose a novel, energy-efficient approach to deep learning-based EEG analytics, designed to enhance wireless information transfer for industrial IoT applications. By applying an autoencoder to sample the EEG data and incorporating a hidden layer to simulate a noisy communication channel, we assessed the energy efficiency and reliability of the transmission. Our results demonstrate that the chosen network topology and parameters significantly affect not only data fidelity but also energy consumption, thus providing valuable insights for the development of sustainable and efficient wireless EEG systems in industrial IoT environments. A key aspect of our study is related to symmetry. Our results demonstrate that the chosen network topology and parameters significantly impact not only data fidelity but also energy fidelity and energy consumption, thus providing valuable insights for the development of sustainable and efficient wireless EEG systems in industrial IoT environments. Furthermore, we realized that the EEG data showed mildly marked symmetry. Neural networks must also exhibit asymmetric behavior for better performance.

show abstract

Section: M2m Technologies With Application To Bci and Eegmentioning

confidence: 99%

Energy-Efficient Deep Neural Networks for EEG Signal Noise Reduction in Next-Generation Green Wireless Networks and Industrial IoT Applications

Kumar,

Chakravarthy,

Nanthaamornphong

2023

Symmetry

View full text Add to dashboard Cite

show abstract

“…Also, Li et al [29] presented a model of a smart splint manufactured using 3D printing techniques that can sense the changing pressure in the fracture area in order to detect the area of looseness in the cast and display the results on a screen attached to the splint. This splint can also measure the temperature in all cast areas through a temperature sensor that connects the cast to the patient's skin.…”

Section: Introductionmentioning

confidence: 99%

Design of an orthopedic smart splint using nickel-titanium shape memory alloy

Alhialy

Alkhaled²,

Al-Sultan³

et al. 2023

IJEECS

View full text Add to dashboard Cite

<span lang="EN-US">People with broken bones suffer from symptoms of muscular atrophy as a result of a lack of movement, so it was necessary to find effective solutions due to the relative pain they cause and the difficulty of movement after healing. In this paper, we proposed a smart splint made of nickel-titanium shape memory alloys (SMA) wires. These alloys have unique properties compared to other materials, the most important of which is maintaining the original shape during manufacturing at a certain temperature. Temperature, pressure, as well as humidity, were analyzed and monitored while the patient wore the splint to reach the best possible results by using a microcontroller. The results showed that there was a significant improvement for the muscles in a short time when using the proposed splint, as the percentage of qualified muscle recovery increased by more than 70% when using the usual splint. The wires used had an effective role in rehabilitating these muscles by performing a permanent local massage. due to the different diameters of these wires, a different response to temperature change was recorded.</span>

show abstract

“…Although CV-based systems have the advantages of simple equipment and low cost, vision algorithms are inevitably accompanied by the shortcomings of being highly influenced by occlusions and light. More critically, CV-based systems have a single source of data (only image pixel information) and therefore lack the means to robustly monitor the patient’s physiological parameters (e.g., pressure on the affected area), yet the tightness of the splints used for immobilization is an important influence on the outcome of fracture rehabilitation ( Li et al, 2021 ). Therefore, the lack of capability of CV-based systems in this area is the greatest drawback compared to sensor-based rehabilitation training systems.…”

Section: Introductionmentioning

confidence: 99%

Wearable rehabilitation wristband for distal radius fractures

Zha,

Xu,

Cai

et al. 2023

Front. Neurosci.

View full text Add to dashboard Cite

BackgroundDistal radius fractures are a common type of fracture. For patients treated with closed reduction with splinting, a period of rehabilitation is still required after the removal of the splint. However, there is a general lack of attention and low compliance to rehabilitation training during this period, so it is necessary to build a rehabilitation training monitoring system to improve the efficiency of patients’ rehabilitation.MethodsA wearable rehabilitation training wristband was proposed, which could be used in the patient’s daily rehabilitation training scenario and could recognize four common wrist rehabilitation actions in real-time by using three thin film pressure sensors to detect the pressure change curve at three points on the wrist. An algorithmic framework for classifying rehabilitation training actions was proposed. In our framework, an action pre-detection strategy was designed to exclude false detections caused by switching initial gestures during rehabilitation training and wait for the arrival of the complete signal. To classify the action signals into four categories, firstly an autoencoder was used to downscale the original signal. Six SVMs were then used for evaluation and voting, and the final action with the highest number of votes would be used as the prediction result.ResultsExperimental results showed that the proposed algorithmic framework achieved an average recognition accuracy of 89.62%, an average recognition recall of 88.93%, and an f1 score of 89.27% on the four rehabilitation training actions.ConclusionThe developed device has the advantages of being small size and easy to wear, which can quickly and accurately identify and classify four common rehabilitation training actions. It can easily be combined with peripheral devices and technologies (e.g., cell phones, computers, Internet) to build different rehabilitation training scenarios, making it worthwhile to use and promote in clinical settings.

show abstract

Design of an intelligent medical splint with 3D printing and pressure detection

Cited by 4 publications

References 1 publication

Energy-Efficient Deep Neural Networks for EEG Signal Noise Reduction in Next-Generation Green Wireless Networks and Industrial IoT Applications

Energy-Efficient Deep Neural Networks for EEG Signal Noise Reduction in Next-Generation Green Wireless Networks and Industrial IoT Applications

Design of an orthopedic smart splint using nickel-titanium shape memory alloy

Wearable rehabilitation wristband for distal radius fractures

Contact Info

Product

Resources

About