Objective: Achieving better surgical outcomes in cases of traumatic bone fractures requires postoperative monitoring of changes in the growth and mechanical properties of the tissue and bones during the healing process. While current in-vivo imaging techniques can provide a snapshot of the extent of bone growth, it is unable to provide a history of the healing process, which is important if any corrective surgery is required. Monitoring the time evolution of in-vivo mechanical loads using existing technology is a challenge due to the need for continuous power while maintaining patient mobility and comfort. Methods: This paper investigates the feasibility of self-powered monitoring of the bone-healing process using our previously reported piezo-floating-gate (PFG) sensors. The sensors are directly integrated with a fixation device and operate by harvesting energy from microscale strain variations in the fixation structure. Results: We show that the sensors can record and store the statistics of the strain evolution during the healing process for offline retrieval and analysis. Additionally, we present measurement results using a biomechanical phantom comprising of a femur fracture fixation plate; bone healing is emulated by inserting different materials, with gradually increasing elastic moduli, inside a fracture gap. Conclusion: The PFG sensor can effectively sense, compute, and record continuously evolving statistics of mechanical loading over a typical healing period of a bone, and the statistics could be used to differentiate between different bone-healing conditions. Significance: The proposed sensor presents a reliable objective technique to assess bone-healing progress and help decide on the removal time of the fixation device.

By incorporating sensing capabilities in passive radio-frequency identification (RFID) tagging technology it is possible to extend the coverage of Internet-of-Things (IoT) to monitor the health of different segments of a large civil infrastructure like pavement highway, buildings or a multi-span bridge. The challenge in this regard is to deliver energy to the RFID sensors that are embedded inside the structures in a manner that they can continuously sense for occurrence of any rare structural events. This paper summarizes some of the progress that has been made to-date in the area of self-powered RFID sensor networks within the concept of IoT. The core sensor uses a self-powering method which directly harvests computational and storage energy from slight strain-variations in the structure. The event signatures can then be stored on a non-volatile memory and remotely retrieved at a later period of time. In this “sense now retrieve later” paradigm, self-powering is only used for continuous sensing and data-logging of essential statistics; whereas, data retrieval and reconfiguration is achieved using a low-cost commercial RFID system. Another advantage of using a commercial RFID system for data retrieval is that the related standards and FCC compliance are well established and the technology can be easily integrated with other IoT network infrastructure.

Continuous, battery-free operation of sensor nodes requires ultra-low-power sensing and data-logging techniques. Here we report that by directly coupling a sensor/transducer signal into globally asymptotically stable monotonic dynamical systems based on Fowler-Nordheim quantum tunneling, one can achieve self-powered sensing at an energy budget that is currently unachievable using conventional energy harvesting methods. The proposed device uses a differential architecture to compensate for environmental variations and the device can retain sensed information for durations ranging from hours to days. With a theoretical operating energy budget less than 10 attojoules, we demonstrate that when integrated with a miniature piezoelectric transducer the proposed sensor-data-logger can measure cumulative “action” due to ambient mechanical acceleration without any additional external power.

Due to the current epidemic levels of sport-related concussions (SRC) in the U.S., there is a pressing need for technologies that can facilitate long-term and continuous monitoring of head impacts. Existing helmet-sensor technology is inconsistent, inaccurate, and is not economically or logistically practical for large-scale human studies. In this paper, we present the design of a miniature, battery-less, self-powered sensor that can be embedded inside sport helmets and can continuously monitor and store different spatial and temporal statistics of the helmet impacts. At the core of the proposed sensor is a novel time-dilation circuit that allows measurement of a wide-range of impact energies. In this paper an array of linear piezo-floating-gate (PFG) injectors has been used for self-powered sensing and storage of linear and rotational head-impact statistics. The stored statistics are then retrieved using a plug-and-play reader and has been used for offline data analysis. We report simulation and measurement results validating the functionality of the time-dilation circuit for different levels of impact energies. Also, using prototypes of linear PFG integrated circuits fabricated in a 0.5 μm CMOS process, we demonstrate the functionality of the proposed helmet-sensors using controlled drop tests.

In this paper we present an adaptive synaptic array that can be used to improve the energy-efficiency of training machine learning (ML) systems. The synaptic array comprises of an ensemble of analog memory elements, each of which is a micro-scale dynamical system in its own right, storing information in its temporal state trajectory. The state trajectories are then modulated by a system level learning algorithm such that the ensemble trajectory is guided towards the optimal solution. We show that the extrinsic energy required for state trajectory modulation can be matched to the dynamics of neural network learning which leads to a significant reduction in energy-dissipated for memory updates during ML training. Thus, the proposed synapse array could have significant implications in addressing the energy-efficiency imbalance between the training and the inference phases observed in artificial intelligence (AI) systems.