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The Smart Clock – Mantra’s IoT Experiment.

IoT is emerging as a disruptive technology and is growing significantly, as consumers, businesses, and governments recognize the benefit of connecting inert devices to the internet.

With breakneck speed, the Internet of Things (IoT) has branched out of the B2B and industrial markets where its concept first took root and exploded into the consumer market in a major way. IoT extends beyond just “smart homes,” that can gather useful data and automate some of our everyday activities. It seems like almost every consumer device will be equipped with IoT connectivity. It joins sensors, devices, data and connectivity together to make the Internet a mesh of Things which can interact, exchange, act with intelligence and transfers data inside networks. Though it is still evolving but it’s promising and pragmatic applications are seen in all verticals, there are already a number of consumer products that use the IoT technology.

With companies joining this new epoch in technology, we also are building our “Smart Clock” (right now in prototype), which is inspired by Ingrein Clock (a kickstarted project).

For Quick Prototyping we started with readymade circuit boards Raspberry PI / Arduino / Particle.io,  including various sensors to have a fair idea about the components and modules required to build the final product . We also started minifying the board and breaking down the circuit to absolute components that are required in building Smart Clock. Before proceeding further let us know

  • What is Raspberry PI?
    It’s a mini computer with GPIO pins. The device is quite powerful and is able to run complete Operating Systems like Linux. It simplifies a lot of hardware and software specs altogether.
    We just need to connect any hardware module to the GPIO pins and then program Raspberry (in any language) though Python has a lot of libraries for raspberry.

The device will cost around 2-3K. One can get started using Raspberry PI soon.

  • What is Arduino?
    Arduino Board has a micro-controller and a set of digital and analog I/O pins to communicate with other hardware devices.
    Arduino is more hardware oriented since it does not come up with installed Operating System.Arduino also provides you its own programming development toolkit where you could submit your code and the software mounts the code to micro-controller. We do not have language choices here but one must know the basics of C++. We can turn this Arduino into any smart device we want to and we can use multiple sensors. Optimized-IMG_20160726_153747

While building this Smart Clock, we did couple of experiments on Raspberry PI and Arduino. For example, we face problem to check whether the meeting room is empty or not, for that we added PIR motion sensors to Raspberry PI and programmed it in Python.

The next task was to exchange data between Raspberry PI and server so one could get the status of the room from his mobile. We implemented Mqtt/Mosca for this (node.js). Now if there was any motion, the PI would send a message to the server and the same could be retrieved on the mobile. This was a simple exercise just to get started.

The next current task we are doing is trying to put minimal required components and sensors together to build a Smart Clock (expected to be changed). Optimized-IMG_20160726_154021

Mantra’s Smart Clock:
A smart clock could read your notification alerts and check other daily tasks.

Currently we have picked one feature that is the clock could tell whether someone from the family is about to arrive. For example at evening, if you are coming from office, as soon as you are near your home- around 200-400 metres away, the clock would notify about your incoming and hence someone at your home could start preparing beforehand whatever you want – food/snacks etc. The clock will be connected to internet and will come with an app that keeps pushing user state to the servers.IMG_20160726_153904

Smart Clock quick points:
– Connectivity: the clock will come with an app which will be used
to connect with clock using Bluetooth. The clock will be configured
using this app such as connecting it with internet and other basic
setttings.
– Currently we are only focusing on very few activities such as
notifying family members about activities such as notifying member is about to arrive ,
weather and app notifications

Prototype Technical Specification:

Connectivity: Bluetooth/Wifi
Sensors:          PIR motion detector
Board:             RaspberryPI/Arduino

The project is currently under progress. We are customizing the circuit board with lesser components, what are needed only.

For a complete updates on “Smart Clock” and other latest technology, approach Mantra Labs at hello@mantralabsglobal.com.

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Conversational UI in Healthcare: Enhancing Patient Interaction with Chatbots

As healthcare becomes more patient-centric, the demand for efficient and personalized care continues to grow. One of the key technologies that have gained traction in this domain is Conversational UI (CUI) — a user interface where interactions occur through natural language, often with the help of chatbots. For developers, building a robust CUI in healthcare requires a balance of technical proficiency, understanding of the healthcare landscape, and empathy toward patient needs. Let’s explore how CUI can improve patient interactions through chatbots and what developers should consider during implementation.

Why Conversational UI is Gaining Popularity in Healthcare

From scheduling appointments to answering medical queries, healthcare chatbots have become vital tools for enhancing patient engagement and streamlining healthcare workflows. Conversational UIs enable these chatbots to interact with patients naturally, making them accessible even to non-tech-savvy users. By incorporating AI and NLP (Natural Language Processing), chatbots can now simulate human-like conversations, ensuring patients receive timely, relevant responses. 

Image credit: https://www.analytixlabs.co.in/blog/ai-chatbots-in-healthcare/ 

Key Areas Where Chatbots Are Revolutionizing Healthcare

  1. Appointment Scheduling and Reminders – Chatbots can automatically schedule appointments based on patient availability and send reminders before the visit, reducing no-show rates. For developers, this feature requires integration with hospital management systems (HMS) and calendar APIs. The challenge lies in ensuring secure and real-time data transfer while adhering to healthcare compliance standards like HIPAA.
  1. Medical Query Resolution– Chatbots equipped with NLP can answer common patient questions related to symptoms, medications, and treatment plans. This reduces the burden on healthcare providers, allowing them to focus on more critical tasks. Developers working on this feature need to consider integrating medical databases, such as SNOMED CT or ICD-10, for accurate and up-to-date information.
  1. Patient Monitoring and Follow-ups – Post-discharge, chatbots can monitor a patient’s condition by regularly asking for health updates (e.g., vital signs or medication adherence). Developers can integrate IoT devices, such as wearable health monitors, with chatbot platforms to collect real-time data, providing healthcare professionals with actionable insights.
  1. Mental Health Support – Chatbots have shown promise in offering mental health support by providing patients with an outlet to discuss their feelings and receive advice. Building these chatbots involves training them on therapeutic conversational frameworks like Cognitive Behavioral Therapy (CBT), ensuring they offer relevant advice while recognizing when a human intervention is required.

Key Considerations for Developers

1. Natural Language Processing (NLP) and AI Training

NLP plays a pivotal role in enabling chatbots to understand and process patient queries effectively. Developers must focus on the following:

Training Data: Start by gathering extensive datasets that include real-life medical queries and patient conversations. This ensures that the chatbot can recognize various intents and respond appropriately.

Multi-language Support: Healthcare is global, so building multi-lingual capabilities is critical. Using tools like Google’s BERT or Microsoft’s Turing-NLG models can help chatbots understand context in different languages.

Contextual Understanding: The chatbot must not just respond to individual queries but also maintain the context across the conversation. Developers can use contextual models that preserve the state of the conversation, ensuring personalized patient interactions.

2. Security and Compliance

Healthcare chatbots handle sensitive patient information, making security a top priority. Developers must ensure compliance with regulations such as HIPAA (Health Insurance Portability and Accountability Act) in the U.S. and GDPR (General Data Protection Regulation) in Europe. Key practices include:

  • Data Encryption: All communication between the chatbot and the server must be encrypted using protocols like TLS (Transport Layer Security).
  • Authentication Mechanisms: Implement two-factor authentication (2FA) to verify patient identity, especially for sensitive tasks like accessing medical records.
  • Anonymization: To avoid accidental data breaches, ensure that the chatbot anonymizes data where possible.

3. Seamless Integration with EHR Systems

For chatbots to be truly effective in healthcare, they must integrate seamlessly with Electronic Health Record (EHR) systems. This requires a deep understanding of healthcare APIs like FHIR (Fast Healthcare Interoperability Resources) or HL7. Developers should aim to:

  • Enable Real-time Updates: Ensure that chatbot interactions (e.g., new appointment schedules, and symptom checks) are instantly reflected in the patient’s EHR.
  • Avoid Data Silos: Ensure that all systems (EHR, chatbot, scheduling system) can communicate with each other, eliminating data silos that can lead to fragmented patient information.

4. Scalability and Performance Optimization

In healthcare, downtime can be critical. Developers need to ensure that chatbots are scalable and capable of handling thousands of patient interactions simultaneously. Using cloud-based platforms (AWS, Google Cloud) that offer auto-scaling capabilities can help. Additionally, performance optimization can be achieved by:

  • Caching Responses: Store frequently used responses (such as FAQs) in memory to speed up interaction times.
  • Load Balancing: Implement load balancers to distribute incoming queries across servers, ensuring no single server is overwhelmed.

Tools and Platforms for Building Healthcare Chatbots

Several tools and platforms can aid developers in building healthcare chatbots with conversational UIs:

  1. Dialogflow (Google): Offers pre-built healthcare intents and integrates with Google Cloud’s healthcare APIs.
  2. Microsoft Bot Framework: A scalable platform that integrates with Azure services and offers AI-driven insights.
  3. Rasa: An open-source NLP tool that provides flexibility in creating highly customized healthcare bots.

Conclusion

Conversational UI in healthcare is transforming patient care by offering real-time, scalable, and personalized interactions through chatbots. However, for developers, building these systems goes beyond programming chatbots — it involves understanding the unique challenges of healthcare, from regulatory compliance to seamless integration with hospital systems. By focusing on NLP capabilities, ensuring security and privacy, and integrating with existing healthcare infrastructure, developers can create chatbots that not only enhance patient interaction but also alleviate the burden on healthcare providers.

References

  1. NLP in Healthcare: Opportunities and Challenges
  2. HIPAA Compliance for Chatbots

About the Author:

Shristi is a creative professional with a passion for visual storytelling. She recently transitioned from the world of video and motion graphics to the exciting field of product design at Mantra Labs. When she’s not designing, she enjoys watching movies, traveling, and sharing her experiences through vlogs.

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