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![Figure 3. Agent by Ali et al [30].](profile/Roel-Boumans/publication/360202730/figure/fig3/AS:1155678936793094@1652546738896/Agent-by-Ali-et-al-30_Q320.jpg)
![Figure 5. Agent Louise (left) and Charlotte (right) by Wargnier et al [24].](profile/Roel-Boumans/publication/360202730/figure/fig5/AS:1155678936809479@1652546738933/Agent-Louise-left-and-Charlotte-right-by-Wargnier-et-al-24_Q320.jpg)
Background:
Older adults often have increasing memory problems (amnesia), and approximately 50 million people worldwide have dementia. This syndrome gradually affects a patient over a period of 10-20 years. Intelligent virtual agents may support people with amnesia.
Objective:
This study aims to identify state-of-the-art experimental studies wit...
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Citations
... To evaluate individual studies, this tool applies 16 items. [5] * * * * * * * * * [35] * * * * * * * * [38] * * * * * * * * * [39] * * * * * * * [40] * * * * * * * * * * [42] * * * * * * * * * * [48] * * * * * * * [49] * * * * * * * [50] * * * * * * * * [51] * * * * [52] * * * * * * * [53] * * * * * * * * * * * [54] * * * * * * * * * * * [55] * * * * * * * * * * [56] * * * * * * * * [57] * * * * * * * * [58] * * * * * * * * [59] * * * * * * * * * * [60] * * * * * * * * * * [61] * * * * * * * [62] * * * * * * * * * * * * [63] * * * * * * * * * * [64] * * * * * * * * * * * 10. Did the review authors report on the sources of funding for the studies included in the review? ...
... While there is considerable diversity in the effectiveness of chatbots, they have also shown potential for effective use in public health situations [55,57,61,77]. They have been employed successfully in smoking cessation programs [76], COVID risk assessment, monitoring exposure analysis and notifications, and even vaccination purposes [53][54][55]75]. ...
Introduction: Chatbots, computer programs emulating natural language conversations, have gained attention in healthcare. Recent advances address issues like obesity, dementia, oncology, and insomnia. A comprehensive assessment of their utility is essential for widespread adoption. This study aims to summarize chatbots' role in healthcare.
Material and Methods: The methodology involved a systematic review of English-language literature up to May 8, 2023, from databases of Embase, PubMed, Web of Science, and Scopus. Selection followed a two-step process based on inclusion/exclusion criteria. The PRISMA checklist and AMSTAR-2 tool ensured quality.
Results: The review encompassed 38 articles. Findings reveal chatbots primarily promote healthy lifestyles, improving mental well-being. They are widely used for treatment, education, and screening due to their accessibility.
Conclusion: Chatbots hold transformative potential in healthcare, especially in mental health, cancer management, and public health. They are poised to revolutionize the industry, offering innovative solutions and improving patient outcomes.
... Studies have shown that this technology can accurately identify emotional states in speech with high reliability, enabling caregivers to provide appropriate support and interventions for elderly individuals. A study [3] found that automated speech analysis can detect signs of depression and anxiety in the speech of elderly individuals with an accuracy rate of up to 88%. Additionally, in [4] authors found that speech-based interventions, including those using automated speech to emotion recognition technology, can improve emotional regulation and reduce symptoms of depression and anxiety in elderly individuals. ...
... These models can learn representations of speech signals from raw audio data, eliminating the need for manual feature extraction, and can achieve high accuracy rates when trained on large datasets. The most commonly used deep learning models for speech emotion recognition include Convolutional Neural Networks (CNNs) [7], Recurrent Neural Networks (RNNs) [3], Deep Belief Networks (DBNs) [8], Autoencoders [9], and Hybrid models [6]. These models have shown promising results in various studies and applications, including emotion recognition in human-robot interaction [10], emotion detection in call center conversations [3], and emotion analysis in mental health research [1]. ...
... The most commonly used deep learning models for speech emotion recognition include Convolutional Neural Networks (CNNs) [7], Recurrent Neural Networks (RNNs) [3], Deep Belief Networks (DBNs) [8], Autoencoders [9], and Hybrid models [6]. These models have shown promising results in various studies and applications, including emotion recognition in human-robot interaction [10], emotion detection in call center conversations [3], and emotion analysis in mental health research [1]. With the increasing availability of speech data and computing resources, deep learning models are expected to continue playing a crucial role in advancing the field of speech emotion recognition. ...
Speech emotion recognition holds significant importance as it enables machines to understand and respond to human emotions, enhancing human-computer interaction and personalized experiences. Accurate identification and interpretation of emotional states from speech signals enable various benefits, including enhanced personalized experiences, effective monitoring of mental health, and improved human-computer interfaces. However, recognizing emotions from speech is a difficult task primarily because there exists a significant disparity between acoustic features and human emotions. Both vocal cues and spoken words play significant roles in determining a person’s emotional state. Therefore, in order to accurately identify human emotions from speech, it is essential to extract distinct and meaningful acoustic features. In this paper, we propose a novel approach to infer human emotional states. Human emotional state recognition has a wide range of applications ranging from customer service to mental health. Our proposed approach extracts a set of features from the speech signals, and employs a framework known as deep stride convolutional neural network using bi-directional LSTM. Our proposed model achieved a high accuracy of 95% which is almost 20% higher when compared to the state of the art on the RAVDESS dataset, while also minimizing the loss.
... Finally, by including review studies on chatbots, it may reveal whether studies similar to ours have been conducted in the chatbot domain. To summarize the previous review studies carried out ,Some reviews focus on specific user groups, such as older adults [46], while others are more broadly oriented towards general usage [47]. As the field of VA is still relatively new, most of these review studies that were carried out on UX of VA have focused on the applicability of VA in a specific areas like education and healthcare [44]. ...
... [ 46] This study identifies the state-of-the-art experimental studies with a chatbot with a screen display capable of verbal dialogues, focusing on older adults with amnesia. ...
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... Lucas et al. [45] found that people with combat-related mental health conditions (e.g., post-traumatic stress disorder) were more likely to disclose their symptoms to a CA compared to a health professional or an anonymous health assessment. For people with cognitive impairments, CAs have been shown to have positive usability in people with memory problems [11], empower both the individual and their care partners [77], and potentially serve as a monitor of cognitive impairments through entertainment [22]. Rampioni et al. [62] reviewed the literature on CAs' usability for people with dementia and found that individuals were naturally engaged and attentive when using CAs. ...
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