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    Use cases

    Keesense Smart Textile Predictive Solution Reference Design HOTS


    A predictive algorithm for data fusion analytics

    Importance of Data Fusion Analytics for Wearable Sensors


    According to a study published in Medical Engineering & Physics, wearable sensors are at the cusp of becoming truly pervasive and ubiquitous, with healthcare applications in a variety of areas including physiological monitoring, ambulatory monitoring  and falls detection. However, the richness of data available using wearable sensors presents challenges in the way that it is processed to provide accurate and relevant outputs. To fully exploit this data for the purposes of healthcare monitoring, data fusion techniques that interpret the complex multidimensional information can be employed to make inferences and improve the accuracy of the output.

    A woman doing sport and her health data being monitored

    Chronolife’s HOTS Technology for Multiparametric Sensor Data Analytics

    Chronolife has pioneered a patented neuromorphic algorithm called HOTS (Hierarchy Of event-based Time Surfaces), a machine learning predictive algorithm capable of continuously analyzing complex, multiparametric data streams on low bandwidth such as smartphones or tablets, and detecting pattern deviations. HOTS can be ported and integrated with a wide range of mobile devices and platforms for local analysis and relevant alerts generation.

    Embed Chronolife’s HOTS Into Your Devices & Health IT Programs for data fusion analytics

    Chronolife offers HOTS as an embedded predictive algorithm that can be easily integrated with any of your data-collecting devices, IoT platforms, smart objects across a variety of purposes and programs:

    • Consumer smart objects such as smart watches, rings, and other wearable devices
    • Connected vehicle technology helps make driving safer
    • Health tracking wearable devices for pets
    • Athlete monitoring for training load, performance, and rehabilitation
    • Smart home systems for improving household wellness and safety
    • Internet of Medical Things-enabled MRI scanners for medical imaging
    • Research programs that require data fusion analytics on low bandwidth

    Some specific example use cases scenarios include:

    • For general health monitoring, HOTS can be integrated with a plethora of connected home health devices such as connected watches, smart weights, and glucose/blood pressure monitors to synthesize and analyze physiological sensor data input from a variety of sources, in order to gather a global view of the users’ health and wellness status.
    • For hospitals and specialized medical care, HCPs can leverage HOTS, which can integrate patient questionnaires with their medical histories and comorbidities, to look for specific anomalies, triggers, warning signs based on deviation patterns particular to pathology cases. Chronolife end-to-end offering means that we’ll collaborate with you in customizing and training the predictive algorithm based on your patients’ unique medical use cases and data requirements agnostic of medtech devices.
    A diagram of all the use cases that HOTS can covered