Wearable augmentative and alternative communication device for paralysis victims using Brute Force Algorithm for pattern recognition

Garcia, Ramon G.; Ibarra, Joseph Bryan G.; Paglinawan, Charmaine C.; Paglinawan, Arnold C.; Valiente, Leonardo D.; Sejera, Marianne M.; Bernal, Mario V.; Cortinas, Walfred J.; Dave, Joshua M.; Villegas, Maryel C.

doi:10.1109/hnicem.2017.8269554

Cited by 9 publications

(14 citation statements)

References 4 publications

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“…This multi-modal access can in turn be advantageous, as users will have a choice to access the device using more than one modality. Breath-activated methods are similarly used with individuals with minimal voluntary controls; however, they require a training step to recognize the selected patterns [51,55]. With regards to BCI access, non-invasive methods, such as EEG, are used due to their non-intrusive nature.…”

Section: Comparison Of Existing Aac Signal Sensing Methodsmentioning

confidence: 99%

“…Touch activated methods also need to incorporate a visual or auditory feedback mechanism to confirm the user's selection, as demonstrated in Table 2 [19]. Breath-activated methods are similarly programmed based on breath thresholds [55], and classification algorithms [51]. The complexities are in turn dependent on the requirements of the APP design together with the selected classification algorithms required for the system operation.…”

Section: Ease Of Programming and Maintainingmentioning

confidence: 99%

“…The system aided the users in LIS, with reported rates of three letters per minute.Microphones could be also used in combination of an AAC interface. The loss of speech abilities associated with SLCN centralizes the usage of microphones around two AAC areas, including speech augmentation of individuals suffering partial loss of speech[54] and breath encoding for individuals with speech disabilities[51,55]. Encoding distinct inhalation and exhalation signals was presented in[55] to produce synthesized machine spoken words (SMSW) through soft and heavy blows represented through four bit combinations of zeros and ones.…”

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confidence: 99%

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Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Speech Disabled Individuals

Elsahar¹,

Hu²,

Bouazza-Marouf³

et al. 2019

Preprint

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High-tech augmentative and alternative communication (AAC) methods are on a constant rise; however, the interaction between the user and the assistive technology is still challenged for an optimal user experience centered around the desired activity. This review presents a range of signal sensing and acquisition methods utilized in conjunction with the existing high-tech AAC platforms for speech disabled individuals, including imaging methods, touch-enabled systems, mechanical and electro-mechanical access, breath-activated methods, and brain computer interfaces (BCI). The listed AAC sensing modalities are compared in terms of ease of access, affordability, complexity, portability, and typical conversational speeds. A revelation of the associated AAC signal processing, encoding, and retrieval highlights the roles of machine learning (ML) and deep learning (DL) in the development of intelligent AAC solutions. The demands and the affordability of most systems were found to hinder the scale of usage of high-tech AAC. Further research is indeed needed for the development of intelligent AAC applications reducing the associated costs and enhancing the portability of the solutions for a real user’s environment. The consolidation of natural language processing with current solutions also needs to be further explored for the amelioration of the conversational speeds. The recommendations for prospective advances in coming high-tech AAC are addressed in terms of developments to support mobile health communicative applications.

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Section: Comparison Of Existing Aac Signal Sensing Methodsmentioning

confidence: 99%

Section: Ease Of Programming and Maintainingmentioning

confidence: 99%

mentioning

confidence: 99%

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Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Speech Disabled Individuals

Elsahar¹,

Hu²,

Bouazza-Marouf³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Microphones could be also used in combination of an AAC interface. The loss of speech abilities associated with SLCN centralizes the usage of microphones around two AAC areas, including speech augmentation of individuals suffering partial loss of speech [60] and breath encoding for individuals with a speech disability [57,61]. Speech augmentation applications, such as “Voiceitt” [62], are currently researched to aid the communication of individuals suffering from Dysarthria or using non-standard forms of speech.…”

Section: Sensing Modalities and Their Functionalitiesmentioning

confidence: 99%

“…On the other hand, breath encoding is being researched to aid the communication of the users lacking speech abilities. Encoding distinct inhalation and exhalation signals is presented in [61] to produce synthesized machine spoken words (SMSW) through soft and heavy blows represented through four-bit combinations of zeros and ones. The classification is achieved based on the threshold values of the generated blows.…”

Section: Sensing Modalities and Their Functionalitiesmentioning

confidence: 99%

Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Individuals with a Speech Disability

Elsahar

Bouazza-Marouf

et al. 2019

Sensors

View full text Add to dashboard Cite

High-tech augmentative and alternative communication (AAC) methods are on a constant rise; however, the interaction between the user and the assistive technology is still challenged for an optimal user experience centered around the desired activity. This review presents a range of signal sensing and acquisition methods utilized in conjunction with the existing high-tech AAC platforms for individuals with a speech disability, including imaging methods, touch-enabled systems, mechanical and electro-mechanical access, breath-activated methods, and brain–computer interfaces (BCI). The listed AAC sensing modalities are compared in terms of ease of access, affordability, complexity, portability, and typical conversational speeds. A revelation of the associated AAC signal processing, encoding, and retrieval highlights the roles of machine learning (ML) and deep learning (DL) in the development of intelligent AAC solutions. The demands and the affordability of most systems hinder the scale of usage of high-tech AAC. Further research is indeed needed for the development of intelligent AAC applications reducing the associated costs and enhancing the portability of the solutions for a real user’s environment. The consolidation of natural language processing with current solutions also needs to be further explored for the amelioration of the conversational speeds. The recommendations for prospective advances in coming high-tech AAC are addressed in terms of developments to support mobile health communicative applications.

show abstract

A study of decodable breathing patterns for augmentative and alternative communication

Elsahar

Bouazza-Marouf

et al. 2021

Biomedical Signal Processing and Control

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Wearable augmentative and alternative communication device for paralysis victims using Brute Force Algorithm for pattern recognition

Cited by 9 publications

References 4 publications

Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Speech Disabled Individuals

Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Speech Disabled Individuals

Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Individuals with a Speech Disability

A study of decodable breathing patterns for augmentative and alternative communication

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