The majority of eldercare devices gather only basic sensor data or rely on user interaction for alerts. Without autonomous, real-time insight from rich sensing and AI, they are at best only partial solutions. But implementing AI effectively in real-world settings is non-trivial. Challenges include filtering irrelevant data to prevent false alarms, accommodating normal differences amongst individuals, and maintaining the privacy of monitored users through local sensor processing versus cloud streaming of sensitive live data.
SensiML excels at creating compact, autonomous algorithms for rich sensors such as audio, motion, and biosensors. Our seasoned team has nearly a decade of experience along with many datasets and models for deriving valuable insight from human activity. SensiML's on-device algorithms perform accurately and adapt to individual variances without dependence on user intervention or cloud processing. Our experience includes enabling usages such as:
Client follow-through and retention have been perennial issues for PTs as individuals struggle to make the necessary lifestyle changes, skip appointments, and fail to perform (or perform correctly) critical home exercises between sessions. Now with the COVID-19 pandemic, the obstacles to safe in-office visits only further challenge practitioners to achieve results. Remote sessions and telehealth visits place more reliance on technology to suffice where direct in-person sessions are not possible.
SensiML pioneered the use of AI tools for building next-gen wearables capable of motion analytics. The SensiML Analytics Toolkit empowers teams without AI expertise to create sophisticated machine learning wearable algorithms taught by example for proper and improper movement form. SensiML also maintains a cadre of models and datasets on sports motion, ergonomics, and exercise movement.
Just a few proven examples include:
Existing solutions dependent on cloud-based AI suffer from network latency, connectivity outages, and privacy concerns that undermine trust and confidence. Remote data center analytics and server algorithms only work when networks are functioning and delivering minimum throughput performance that supports the transmission of large volumes of raw edge sensor data from many sensor endpoints. This increases the risk of failure in mission-critical health and public safety applications where performance assuredness is a must.
SensiML's edge AI toolkit allows broad-scale screening to be intelligently partitioned with real-time endpoint sensor AI algorithms. SensiML enabled sensor endpoints can thus be transformed from 'dumb' data collectors, to intelligent event detectors abstracting sensitive and voluminous raw data to only the insights of critical interest. Resulting applications can therefore respond faster, with minimal network requirements, and enhanced data privacy. Examples built by SensiML include: