Role of Artificial Intelligence in Wearable Device App Development

Wearable devices such as smartwatches, fitness trackers, medical devices etc. collect huge amounts of data via sensors, including heart rate, motion, temperature and more. With the integration of artificial intelligence (AI) in these devices, they have evolved into sophisticated tools like virtual personal trainers and smart gym equipment that not only track and capture such data but also provide real-time insights, predictions and assist in personalizing the user experience, their health, behavior and life decisions. Enabled with AI development services, they effectively analyze health trends, improve fitness routines and track mental and physical well-being. 

A recent U.S. government report indicated that every 2 in 5 U.S. adults are impacted by chronic diseases such as obesity, heart conditions, diabetes etc. The COVID-19 pandemic, which marked widespread quarantine and social isolation, promote these health issues across the globe. These conditions are also prevalent among young adults aged above 20 years, out of which 43% were men and 42.1% were women, contributing to approximately US $173 billion in terms of medical expenditure during the pandemic. As of 2022, the global market value of AI in fitness and wellness was reported to be US $7.8 billion, which is forecasted to reach US $30.56 billion by 2030, surging at a CAGR of 20.5% during this timeframe. 

Let us explore how involving AI can enhance wearable app development with its extensive processing power and ability to interpret huge datasets swiftly. Apart from that, in this blog, we will also go through the models used, steps involved in developing such devices, applications, use cases, benefits, as well as challenges involved in such integration. 

Surging market size of wearable device app development from 2019 to 2032 

Steps of AI Integration in Wearable App Development 

Given below are the steps of the development of wearable device applications with AI integration. 

1. Functionalities: The first step involves defining the purpose and functionality of the wearable health monitoring device application, whether it is restricted to fitness tracking, health monitoring or a smartwatch. The developers of the smart device must efficiently identify the user requirements, needs and objectives. This is because the use cases of AI-based wearables can be diverse, ranging from chronic disease management, smart fitness coaching and personalization, mental health monitoring and medical emergency detection. Developers must clearly define the problem statement that the application aims to resolve and thereafter focus on the specific AI-based features that can provide value. 

2. Sensors: The next step involves selecting the right kind of sensors during wearable application development that are embedded in the same for collecting relevant data. Common types of sensors found in AI-powered wearables include accelerometers, gyroscopes, global positioning systems, heart rate monitors, temperature sensors, thermistors, electrocardiogram sensors and others. It is important to note that AI models require high-quality data to train on and learn from; thus, selecting the sensor type for meaningful data collection becomes an essential step in the process. For example, motion sensors and heart rate monitors are vital for fitness and activity level tracking, while advanced ECG sensors, blood oxygen or sugar level sensors can be used for monitoring health. 

3. Data Collection: After the wearable device is equipped with necessary sensors, the third step involves data collection and preprocessing, where raw data featuring noise, inconsistency or incompleteness is collected. Furthermore, the data undergoes preprocessing, including outlier removal, normalization, aggregation over time such as daily step counts, average heart rate etc., and cleaning to ensure a suitable format for analysis and meaningful metric generation. 

Examples of wearable app development and devices 

4. AI Models: In this step, machine learning (ML) algorithms such as supervised, unsupervised or deep learning (DL) etc. are used to develop AI models that analyze data, make prompt predictions and provide insights based on identified patterns, for example, potential heart problems can be predicted by these models based on real-time data, such as by Oura Ring. The step, in general involves data labeling which can be automated or manual in case of supervised models, such as diagnoses, activity types and health conditions in a medical technology context. 

Furthermore, developers need to select an appropriate ML or DL algorithm as per the given problem statement. In cases of detection of abnormal heart rates, anomaly detection models can be used, while convolutional neural networks (CNNs) may be preferred for activity classification. The model then undergoes training where large datasets are fed into the same to develop the trait of identifying patterns and making predictions, such as health trend datasets obtained from various users. Ultimately, the model is evaluated using test data for determining its accuracy rate using metrics such as precision, recall and F1 score for measuring performance, followed by model deployment in a wearable app. It then starts processing real-time data and offering insights and predictions. 

Supervised learning models including classification models like decision trees, random forest, support vector machines, regression models etc. are widely used in wearables. For example, in classifying if a user’s heart rate is normal or indicates potential health risk, predicting continuous values like number of calories burned by Whoop Strap, estimating the user’s recovery time after exercise and more. Unsupervised learning models like K-means clustering are also commonly used where the goal is to identify patterns and group data into specific segments/clusters based on user’s activity levels. Deep learning models including CNNs, recurrent neural networks (RNNs) etc. are used to process sensor data as a part of medical device design and development, where CNNs effectively process image data like ECG signals, while RNNs analyze time-series data like activity tracking over a time period. Reinforcement learning is particularly used in scenarios where wearable app development service involve real-time feedback provision to the user and the app aims to optimize user behavior through personalized workout recommendations as per their progress etc. 

5. UI/UX: The user interface needs to be well-designed, minimalistic, intuitive and easy to use so that users can effectively interact with the app, access insights and AI-generated recommendations. It should display essential features and metrics such as heart rate, activity levels, sleep patterns etc. in an easily understandable format. It should also showcase push notifications, alerts, dietary or exercise-related recommendations in a manner that does not overwhelm the user and yet adds value. 

6. Testing: This step involves thorough debugging and testing after wearable device app development to ensure that it functions properly, and the AI-driven features are able to provide reliable insights in real-time. The models need to be fine-tuned as per the feedback received to improve their accuracy and performance. 

7. Continuous Improvement: AI-incorporated wearable devices need to undergo continuous updates and improvements since the user data is collected on a regular basis. With new features, trends and patterns developed due to technological advancements, the models need to be retrained on a periodic basis with such integrations. The devices can also be improved in terms of user interface, new ML models, sensors and client requirements over time. 

Use Cases of AI in Wearable Application Development 

Given below are few use cases where integration of AI in wearable devices can improve their overall efficiency – 

Health Monitoring 

Smart wearables can be used for continuous healthcare monitoring, detection of early onset symptoms of diseases and management of chronic conditions like diabetes and cardiovascular issues. AI models are capable of processing data collected through sensors and identifying abnormal patterns. Such insights can be shared with healthcare professionals in real-time as and when observed to alert them regarding potential issues. For example, a wearable app development service post gaining approval by FDA regulations for medical devices, can develop smart devices capable of tracking blood sugar levels, monitoring heart rate and warn regarding potential health-related risks. AI-based algorithms can also track sleep, analyze patterns to detect disorders including sleep apnea, insomnia etc. by monitoring heart rate, breathing patterns, movements during sleep and provide actionable insights. 

Fitness Tracking 

These trackers can also track fitness levels, assist in optimizing performance, and have thus become essential tools for enthusiasts in this field. They track various types of metrics, such as the number of steps taken in a fixed amount of time or per day, calories burned, total distance traveled on foot, as well as intensity of exercise. On top of this, AI can analyze all these types of data for optimizing workouts, recommending periods of rest, and predicting fitness milestones in the long run. It can provide personalized coaching and nutritional recommendations based on the user’s performance history to improve their goals over time. AI in wearable device app development also benefits users by identifying specific activities such as walking, running, cycling for adapting to metrics and providing swift and accurate feedback. 

Few parameters tracked via AI-powered wearable app development service 

Mental Wellness Tracking 

A very important use case of AI integration in wearables includes mental health monitoring. In this, AI-powered wearables can track physiological indicators such as variations in heart rate and skin temperature that basically indicate risen stress or anxiety levels. By processing level-wise data, these devices can comprehend and offer insights into the mental well-being of the user. They can also suggest certain personalized relaxation techniques and mindful practices for improving the same. 

Emergency Alerts 

Wearables can automatically detect emergencies such as heart attacks, seizures, patient falls etc., and alert concerned authorities, family members, or emergency responders regarding the same. For instance, every wearable app development company ensures that smartwatches have motion detection sensors that can trigger emergency alerts during sudden falls. 

Voice Assistants 

Integration of gesture recognition and AI-enabled voice assistants into wearables can make them more accessible and improve the overall user experience such that they can easily interact with their device hands-free, for searching any kind of information, be it the latest weather report, setting reminders and tracking progress in fitness. 

Benefits & Challenges of AI in Wearable App Development 

Benefits 

• AI enables these devices to generate personalized or tailored recommendations, predict diseases and insights based on specific and individual user data. 

• With real-time processing abilities and a swift feedback system, AI enables eased fitness tracking, health monitoring, dietary restrictions and fall or other types of emergency detection. 

• AI-powered wearables contribute to improved health outcomes by identifying patterning symptoms, detecting the earliest signs of illness, optimizing fitness routines, enabling remote consultations, eliminating the need for urgent tests and promoting the overall well-being of the user. 

• A wearable app development company can ensure through voice assistants that the users interact with these devices without touching the same for an effective hands-free experience. 

• These devices also provide contextual insights where they easily correlate external factors such as weather and temperature to suggest the right kind of exercise, provide measures to avoid very high noise levels in surroundings to avoid impairing etc. 

Challenges 

• The major concern of integrating AI in wearable applications is the collection of sensitive, personal health-related data through data brokerage or attacks such as distributed denial of service (DDoS). This raises privacy and security issues and can be tackled with data protection, encryption and compliance with regulations like FDA, GDPR, CE, HIPAA etc., where market approval may also take up a considerable amount of time. 

• Another issue that needs to be considered is related to the accuracy and reliability of insights generated by AI models, especially in the case of healthcare, where biased algorithms, misdiagnosis and false alerts can lead to server consequences, future risks in health recovery and hinder treatment plans. 

• It is also necessary for a wearable app development company to ensure that users get familiar with AI-powered features through detailed manuals. This is because educating users about the proper usage and benefits of integrating AI in wearables still remains a key challenge against their widespread adoption. 

• Ultimately, there are also limited concerns around the low battery life and energy consumption optimization of AI-enabled wearables due to their high computing power that significantly drains the battery over time. 

• Other issues include the high costs of these devices as well as those involved in the integration with electronic health records (EHRs) for accurate diagnosis and treatment. 

Conclusion 

Integration of AI in wearable devices is revolutionizing many industries, especially healthcare, fitness and well-being, in terms of providing tailored real-time insights, improving user experiences and enhancing health outcomes. Although various challenges such as data privacy, accuracy of results generated by AI models, diminished battery life etc. currently pose issues but, they are being addressed over time. Wearable app development on par with AI use cases showcases tremendous benefits, including real-time and precise patient monitoring etc., and as these devices get more and more advanced, interfaces are likely to become smarter and more personalized in terms of features. Future possibilities of the same include predictive health insights, advanced biometric sensors, increased energy efficiency, real-time language translation and seamless integration with other devices, although it is necessary for health-tech companies to balance cost, functionality, and privacy to enhance personalized care. 

KritiKal performs continuous innovation in designing and developing different types of AI-powered wearable devices. It has assisted various SMBs and Fortune 500 companies, such as in vitro micro diagnostic devices, sensor matrix-based wearable diagnostic tools, bio-sensing devices, electrotherapeutic medical devices for pain, insomnia, depression, computer vision applications for healthcare, and many more. As AI continues to evolve, we can expect even more advanced and intuitive wearable technologies that will make our lives healthier, more efficient, and connected. Please get in touch with us at sales@kritikalsolutions.com to know more about our products and realize your AI wearable device-related requirements.

Irshad Alam

Irshad Alam currently works as a Senior Embedded Engineer at KritiKal Solutions. He is an experienced software developer with a decade of work in C++, C, Linux Kernel, Board Design, CAD etc., alongside a proven track record of delivering results and optimizing product performance. With his passion for innovation and commitment to continuous learning, he has helped KritiKal in delivering various major projects.