A deep learning based multimodal interaction system for bed ridden and immobile hospital admitted patients: design, development and evaluation

Islam, Muhammad Nazrul; Aadeeb, Md Shadman; Munna, Md. Mahadi Hassan; Rahman, Md. Raqibur

doi:10.1186/s12913-022-08095-y

Cited by 4 publications

(6 citation statements)

References 59 publications

(32 reference statements)

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“…The term 𝜑 in Equation ( 5) represents a crucial variable that linearly decreases from 2 → 0 and then increases linearly from 0 → 2. This variable is estimated using Equation (7).…”

Section: Proposed Ehboamentioning

confidence: 99%

“…Let us consider ⃗ G s as a colleague. The location of ⃗ b h when updated with respect to ⃗ G s is determined with the help of Equation (7).…”

Section: Proposed Ehboamentioning

confidence: 99%

“…or else, the surrounding area b o h is explored. The above Equation (7) helps in enhancing both "exploitation and exploration" phases. The contribution provided by each employee is modeled by determining their location in the upcoming iteration and is evaluated using Equation (9).…”

Section: Proposed Ehboamentioning

confidence: 99%

“…6 Hence, both wearable wireless sensor devices and smartphones are proven to be ideal data collection sources for monitoring the real-time health conditions of a patient. 7 A large number of real-time HMSs have been developed and are being used in today's digitalized world. 8 The collected data are transferred using gateway devices that utilize wireless communication techniques.…”

Section: Introductionmentioning

confidence: 99%

“…| | | | | | . (7)The term Z in Equation (7) is the count of iterations, and a user-defined variable F is also initialized and is used in Equation(7). The number of iterations taken by the 𝜑 to decrease and increase linearly from 2 → 0 and to 0 → 2 is determined by this variable F. The value for this parameter F is taken as F = 5Z 100 by conducting various experiments.…”

mentioning

confidence: 99%

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An efficient and secure wearable sensor based remote healthcare monitoring system using adaptive dilated transformer Bi‐LSTM with gated recurrent unit

Vishnupriya,

Anusha,

Kayikci

2024

Trans Emerging Tel Tech

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Innovative research works in the healthcare sector keep on advancing every day. As the “Internet of Things (IoT)” keeps on evolving, the application of IoT in the medical field is prominent these days. Utilizing IoT devices, alert messages can be sent directly to medical professionals in case of an emergency. So, monitoring the health condition of an individual using IoT technology has become a popular and beneficial method in today's contemporary medical field. With the help of mobile IoT medical equipment, the technology of smart Healthcare Monitoring System (HMS) is proliferating. By utilizing deep learning and IoT technology, the medical diagnosis system has evolved from direct face‐to‐face visits to the hospital to remote telemedicine method. Most of the data generated by the IoT wearable sensors are highly correlated and may consist of outliers. The extraction of the essential attributes from these data is a complicated task. So, “deep learning and machine learning” techniques are adapted to determine the most relevant and appropriate feature required for efficient diagnosis from the unstructured data produced by the IoT devices and thus help in minimizing the redundancy of unnecessary data. Fusing deep learning methods with healthcare IoT made only the essential details to be available for diagnosis. Therefore, a deep learning‐oriented IoT‐based HMS is executed in this work. With the support of several wearable healthcare devices, the required data are acquired. The encryption of the data acquired from standard sources using Optimal Key‐based Advanced Encryption Standard (OK‐AES) is carried out next to assure the security of the sensitive medical data. The keys for AES encryption are optimally chosen with the aid of the Enhanced Heap‐Based Optimizer Algorithm (EHBOA). The encrypted data is transferred to the “cloud platform” for data storage. Once there is a need for the data, then the encrypted data is initially downloaded from the cloud platform. Then using the same AES scheme, the decryption of the data to attain the original data is carried out. From the retrieved data, the extraction of the crucial attributes is carried out. The extracted features are chosen in an optimized manner and are concatenated with the tuned weights to form the weighted feature matrix. This formulated weighted feature matrix is provided as input to the “Adaptive Dilated Transformer Bidirectional Long Short‐Term Memory (Bi‐LSTM) with Gated Recurrent Unit (GRU) (ADTBi‐LSTM‐GRU) model.” The variables in the ADTBG are optimized using the EHBOA for providing an accurate classification outcome. The classified disease outcome is obtained from the deployed ADTBi‐LSTM‐GRU model. Simulations are done to verify the efficiency and reliability of the implemented deep learning and IoT‐based HMS.

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“…The term 𝜑 in Equation ( 5) represents a crucial variable that linearly decreases from 2 → 0 and then increases linearly from 0 → 2. This variable is estimated using Equation (7).…”

Section: Proposed Ehboamentioning

confidence: 99%

“…Let us consider ⃗ G s as a colleague. The location of ⃗ b h when updated with respect to ⃗ G s is determined with the help of Equation (7).…”

Section: Proposed Ehboamentioning

confidence: 99%