SensiML has demonstrated the ability to enable the next generation of smart building and roadway sensors for modernized efficient urban centers

Smart Workplace Environments

Industry analysts recently forecast that the connected worker device market will exceed $12 billion by 2027, highlighting the value of connected worker tech devices for improving workplace productivity. Factories that embrace human-machine interactivity, AR/VR, and sensing wearables into automation and hybrid man/machine production processes will outcompete those that neglect such opportunities. The current worldwide pandemic has triggered a large-scale rethink of the office workspace, how to achieve more with less, and reduce traditional office footprints utilizing space more efficiently through the use of smart building technology.


Integrating human-machine interfaces and wearable sensors into existing factory automation systems is challenging. Legacy SCADA networks, PLCs, and robotics systems are not typically accommodating of over-the-top devices and applications. Introducing such technologies risks disruption to status quo production processes. Intelligent building services such as image and audio sensing with centralized AI recognition technologies, face resistance from workers fearful of trust and privacy concerns in centrally monitored environments.

Where SensiML Can Help

SensiML's self-contained algorithms execute fully within the endpoint with no processing dependence on existing systems. Resulting events can be interfaced to PLCs and SCADA networks as simple triggers for clean integration in legacy systems. Smart buildings can leverage SensiML enabled sensors with event-level AI outputs from PIR, audio, and presence detectors used to drive HVAC, lighting, security, and building resource dashboards without exposing sensitive sensor data. Proven examples using SensiML include:

  • Occupancy based HVAC/lighting control
  • Predictive HVAC maintenance
  • Smart monitoring of space/room utilization
  • Janitorial service call automation

Smart Cities and Intelligent Infrastructure

Smart city initiatives are sprouting up across the globe with great promise of harnessing the power of machine learning and low-cost sensors to solve some of the biggest urban growth challenges. A variety of applications and approaches have been envisioned and are being piloted. Applications include AI- driven mass transit systems, real-time traffic routing and management, automated recycling, smart utilities and metering, air quality monitoring and pollution management, and emergency preparedness and response.


Complicated and costly infrastructure is required to install and maintain vast sensor networks. Wired connectivity and device power are both costly factors for systems involving tens of thousands of endpoint nodes. Hacking and data privacy are significant risks with sensor data exposed across network communications where centralized analytics are provided in the cloud from ‘dumb’ sensors delivering raw data across public or cellular network infrastructure.

Where SensiML Can Help

SensiML endpoint optimized AI algorithms execute autonomously on endpoints with power profiles consistent with battery-powered, solar charged devices that are simple to install maintain. With local processing of signal data, SensiML enabled smart city sensors can convey real-time insights over low-bandwidth cellular networks like NB-IoT, and LoRA. Proven use cases include:

  • Acoustic event triggers (gunshots, breaking glass, vehicle accidents)
  • Activity recognition (using low-res optical or PIR sensors)
  • Smart road sensors (intelligent road reflectors)

Retail Operations Management

Data-driven store optimization and smart building technology are becoming a reality of the retail sector. The networked store allows the company to respond optimally to customer needs while maximizing ROI within defined spaces, both digitally and physically across channels. By analyzing visitor motion profiles, the building and floorplan layout can be optimized, including intelligent placement/orientation of products, and Human-Centric-Lighting (HCL) and product illumination, all of which leads to improved customer experience, increased sales, and cost savings.


Given the primary motivation for smart store technologies is improved sales and inventory turnover, system cost and ROI is always a foremost factor. Complex systems that rely on expensive network infrastructure, edge servers, and complex installs limit value and extend payback horizons.

Where SensiML Can Help

SensiML enabled smart sensors can combine rich PIR, IR grid arrays, and microphone sensors with local AI processing to provide real-time insight on customer in-store interactions. Furthermore, this insight can be achieved using low-cost and easy to install wireless endpoints. Using SensiML AI in such sensors, solution providers can readily enable:

  • Improved floorspace planning / lighting
  • Optimized product placements from traffic profiles
  • Enhanced security and loss prevention
  • Automated traffic management (ex. dressing rooms, aisles, restrooms, checkout lines)

Use Cases


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.