Healthcare Technology
2 months
Australia
Arsalan
SWI is an AI-powered sentiment analysis tool designed to improve care for elderly individuals who struggle to express their needs. By allowing seniors to record audio messages about their daily activities, SWI bridges the communication gap between them and their children. The tool analyzes these recordings to detect sentiments and emotions, automatically rating their feelings to ensure they receive the best possible care.
Elderly individuals often face difficulties in expressing their needs and emotions, making it challenging for caretakers and family members to provide adequate care and support. Communication gaps between seniors and their children can lead to a lack of awareness about the emotional well-being and daily experiences of the elderly, ultimately affecting the quality of care. Traditional methods of monitoring elderly well-being do not offer real-time, personalized insights into their emotional state, leaving caretakers without the necessary information to make informed decisions about care and support.
The initial idea of SWI was to design an AI-powered emotional monitoring tool for elderly people who depend on caretakers due to their age-related concerns to share their daily activities with their children by just recording audio to ensure that they are getting adequate care.
Tezeract did an in-depth analysis of the idea and turned it into a fully functional AI-powered sentiment analysis tool designed to enhance elderly care by providing real-time emotional monitoring with AI. It allows seniors to share their daily experiences through voice recordings, which are analyzed using AI sentiment analysis to detect emotions and automatically rate their sentiments on a scorecard from “good” to “needs attention.”
With its AI-powered emotional monitoring tool, SWI tracks feedback frequency and provides a monthly sentiment summary via a dashboard, ensuring families can remotely monitor their loved ones’ well-being. This AI sentiment analysis in elderly care bridges the communication gap and ensures seniors receive proper support. SWI is also an innovative example of how AI sentiment analysis for healthcare is improving emotional care and connection between families.
SWI’s AI tool provides families with instant sentiment analysis of elderly care, helping make informed decisions about their loved ones’ emotional well-being.
Automated monthly sentiment summaries and real-time tracking help reduce caregiver burden by 40%, ensuring timely interventions.
Ensuring the AI correctly detects emotions from varying elderly speech patterns and tones requires advanced machine learning algorithms.
Designing a simple, user-friendly app for seniors to easily record and interact with is crucial for accessibility.
Implementing real-time emotional monitoring while securing sensitive data and adhering to privacy regulations is a key development challenge.
We aligned with the business vision to develop a thorough project brief, which included extensive market research, competitor analysis, and relevant data. We then brought the concept to life by defining a detailed list of user stories and features to validate the core assumptions of the MVP.
We started by designing the key screens to show the client the product’s look. After refining the UX/UI through client feedback. Once these primary screens were finalized, we expanded the design to cover all additional screens and delivered the complete UX/UI for the product.
At this stage, our focus shifts to developing the AI solution for fashion brands by designing the architecture of the AI model and training it.
After the product launch, we collected feedback from end-users to refine and enhance the product. We introduced new iterations and features concurrently to ensure an optimal user experience.
Parents can share their daily routine care by recording an audio clip from their mobile device.
Get AI-based sentiment analysis on your parents’ moods, emotions, and feelings.
Children can monitor their parents’ care in real time by watching an automated sentiment chart.
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