Healthcare

Non-invasive, rapid screening applications utilizing intelligent classification of bio-sensing inputs are critical for decision support.

Eldercare and Aging in Place

According to the US Census Bureau’s 2019 population estimates, 54 million people in the United States are age 65 or older. The same aging demographic trends are true worldwide as healthcare is enabling people to live longer. Enabling senior citizens to safely remain active, independent, and in their own homes longer is a significant boost to quality of life. This population can benefit greatly from smart IoT technology that can monitor health, activity, safety, and adherence to necessary daily tasks and provide seniors and their family members with peace of mind while notifying healthcare providers and families of concerns or emergencies.

The CHALLENGE:

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.

Where SensiML Can Help

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:

  • Human motion and gait analysis
  • Fall prevention, detection, and alerting
  • Daily activity recognition
  • Medication adherence and reminders
  • Biosensor trend analysis and alerting

Physical Therapy, Outpatient Rehabilitation, and Sports Medicine

Physical therapists are highly invested in helping patients with health problems limiting their mobility and daily activities to achieve a better quality of life. Similarly, exercise physiologists and sports medicine practitioners seek to help athletes recover from injury as well as to maximize their potential. In either case, the typical regimen combines in-office visits with directed home care exercises for patients to continue on their own. The potential for intelligent wearable devices to aid in this process of ongoing feedback, guidance, and compliance monitoring is significant and now becoming technically achievable. Using a combination of sensors, biometrics, and algorithms to go well beyond 'step counting' and actually analyze motion, AI sensor wearables can provide patients and their healthcare professionals with detailed, valuable data that they can use to further improve treatment, retention, and outcomes.

The CHALLENGE:

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.

Where SensiML Can Help

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:

  • Ergonomics assessment (box lift exercise)
  • Running gait analysis and automated coaching
  • Activity recognition and counting
  • Sports motion classification and feedback

Healthcare Screening and Public Safety

The importance of screening tools to keep people safe in healthcare facilities, public venues, transportation hubs, and other commercial and public spaces is an evolving opportunity for applying intelligent sensing to overcome challenges of human monitoring on a mass scale. The recent COVID-19 pandemic has obviously brought this concern to the forefront worldwide. Whether it be sensor technology used to rapidly detect, protect, or respond to infectious disease spread, gun violence, fire, or natural disaster, there is an important role to be played by intelligent learning devices aiding facility managers, first responders, and healthcare workers in this vital task.

The CHALLENGE:

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.

Where SensiML Can Help

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:

  • Respiratory illness detection from coughs
  • Gunshot detection
  • Firefighter 'man-down' detection
  • Seismic vibration classification / alerts

Use Cases

Consumer

Are You A Maker Or Inspired AI Innovator?

Visit the SensiML Data Depot repository and review available application examples, documentation, and sample datasets. Each application includes summary information on the hardware and sensor used, number of sample captures, and sector.