Smart Workplace Environments
The CHALLENGE:
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:
Smart Cities and Intelligent Infrastructure
The CHALLENGE:
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:
Retail Operations Management
The CHALLENGE:
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:
Use Cases
Ready for the Real World?
Try Proto Edition which comes enabled by default to generate ML models for motion, vibration, gesture recognition, and countless other subsonic sensor (i.e. requiring <10kHz sample rate) real world applications.
