We recently developed a synchronized low energy electronically chopped passive infrared (SLEEPIR) sensor that can detect both stationary and moving occupants by incorporating a liquid crystal (LC) shutter with a traditional passive infrared (PIR) sensor. However, its detection accuracy is still largely impacted by environmental infrared noises. In this paper, we present a Particle Filter (PF) based system-level algorithm that employs a network of SLEEPIR sensors which are installed at different points of interest within an indoor space. The method interprets the incoming observations from the field of view (FOV) of each sensor via the likelihood function to update the state of the PF. The PF output is a probability density function (pdf) that represents the occupancy state of the entire observed space. The sensor location, observation cone, range, observation frequency and historic inter-sensor correlation are the key parameters that contribute to the likelihood function design. Since the method utilizes the historic correlation among sensors, the pairs of correlating sensors often perform self-correction whenever a faulty observation is encountered due to either sensor limitations or due to environmental noise. Occupancy is established through a thresholding function applied to the output pdf of the PF. A lab-based dataset was collected over a period of 360 hours using the SLEEPIR sensor system. Results indicate an average 8.25% occupancy accuracy improvement when compared to the accuracy state delivered by individual SLEEPIR nodes.

The goal of this paper is to provide a novel computing approach that can be used to reduce the power consumption, size, and cost of wearable electronics. To achieve this goal, the use of microelectromechanical systems (MEMS) sensors for simultaneous sensing and computing is introduced. Specifically, by enabling sensing and computing locally at the MEMS sensor node and utilizing the usually unwanted pull in/out hysteresis, we may eliminate the need for cloud computing and reduce the use of analog-to-digital converters, sampling circuits, and digital processors. As a proof of concept, we show that a simulation model of a network of three commercially available MEMS accelerometers can classify a train of square and triangular acceleration signals inherently using pull-in and release hysteresis. Furthermore, we develop and fabricate a network with finger arrays of parallel plate actuators to facilitate coupling between MEMS devices in the network using actuating assemblies and biasing assemblies, thus bypassing the previously reported coupling challenge in MEMS neural networks.

Our recently developed Synchronized Low Energy Electronically chopped Passive Infra-Red (SLEEPIR) sensor node enables the stationary occupancy detection capability of traditional Passive Infra-Red sensors. A Machine Learning (ML) algorithm reports occupancy based on a locally collected dataset from the sensor node. Though promising, the ML algorithm’s detection accuracy depends on the diversity of the collected dataset- provided the dataset contains a wide variety of infrared noise and occupancy patterns. Thus, it is challenging to train a universal ML model that contains all possible patterns. We propose an efficient K-Nearest Neighbor (KNN) occupancy classifier that incrementally adapts to the novel data from the sensor. The proposed algorithm ensures that only the relevant noise and occupancy patterns are learned. The fact that training observations are gathered on the same sensor node where the inference is made, keeps the proposed classifier accurate even with the bounded size of the dataset. A small dataset and an architecture like KNN, both enable the training and inference to be executed on a resource-constrained Internet of Things device. Thus, the proposed On-Device Lifelong Learning approach eliminates the need for over-the-cloud ML model updates. The dataset was collected for two distinct floorplans over two months. Results indicate an average occupancy accuracy improvement of 20.8% compared to a statically trained Long Short-Term Memory (LSTM) model. The proposed KNN model delivers comparable detection accuracy while remaining orders of magnitude faster in terms of computational performance when compared to the LSTM-based occupancy detection algorithm.

In the past decade, different sensing mechanisms and algorithms have been developed to detect or estimate indoor occupancy. One of the most recent advancements is using networked sensor nodes to create a more comprehensive occupancy detection system where multiple sensors can identify human presence within more expansive areas while delivering enhanced accuracy compared to a system that relies on stand-alone sensor nodes. The present work reviews the studies from 2012 to 2022 that use networked sensor nodes to detect indoor occupancy, focusing on PIR-based sensors. Methods are compared based on pivotal ADPs that play a significant role in selecting an occupancy detection system for applications such as Health and Safety or occupant comfort. These parameters include accuracy, information requirement, maximum sensor failure and minimum observation rate, and feasible detection area. We briefly describe the overview of occupancy detection criteria used by each study and introduce a metric called “sensor node deployment density” through our analysis. This metric captures the strength of network-level data filtering and fusion algorithms found in the literature. It is hinged on the fact that a robust occupancy estimation algorithm requires a minimal number of nodes to estimate occupancy. This review only focuses on the occupancy estimation models for networked sensor nodes. It thus provides a standardized insight into networked nodes’ occupancy sensing pipelines, which employ data fusion strategies, network-level machine learning algorithms, and occupancy estimation algorithms. This review thus helps determine the suitability of the reviewed methods to a standard set of application areas by analyzing their gaps.

Exploring the Machine Learning (ML) features and methods based on electroencephalography (EEG) and quantitative electroencephalographic (qEEG) holds the potential to early diagnosis and classification of Parkinson’s disease (PD) and can bring convenience to the decision-making process for healthcare professionals. PD is clinically heterogeneous, with high variability between patients in terms of motor and non-motor symptoms. This nature makes it extremely challenging for early diagnosis, and severity classification using traditionally questionnaire-based standards. EEG and qEEG provide objective signals with rich features that can potentially provide a translational alternative. In the past decade, a host of ML-based features and algorithms have been developed to detect and classify PD in patients using a range of EEG and qEEG datasets. Various methodologies, including graph-CNNs, deep RNNs, and hybrid models, are discussed within the context of early diagnosis and classification, each offering unique insights and performance levels. This review highlights the multifaceted nature of PD diagnosis and classification research. It addresses the challenges and limitations encountered in existing techniques, emphasizing the need for further improvements in accuracy, sensitivity, and specificity. This review provides a standardized insight into the evolving landscape of PD diagnosis and classification. It showcases the remarkable progress that has been made in utilizing EEG and qEEG data while emphasizing the ongoing quest for effective and convenient ML based methods for early diagnosis and classification.

We have previously developed a synchronized low energy electronically chopped passive infrared (SLEEPIR) sensor node that can detect both stationary and moving occupants. In this paper, we present a Bayes Filter (BF) based network-level algorithm that uses a network of SLEEPIR sensor nodes deployed at a residential apartment to estimate occupancy of the entire apartment. The method processes the incoming observations from each of the sensor nodes via a sensor model and transforms these observations into Bayesian updates. The sensor model uses a Markov Decision Process (MDP) formulation to estimate the temporal bounds on the rate of occupancy flow between one occupancy state to another. The overall BF output is a probability density function (pdf) that represents the occupancy state of the entire observed space. The sensor node adjacency matrix and observation frequency are the key parameters that contribute to the sensor model design. The sensor model uses estimated transition time and probability between occupancy states to filter out observations that do not conform to the constraints set forth by the parameters. Occupancy is established through a thresholding function applied to the output pdf of the BF. A dataset was collected at a residential unit over a period of one month using the SLEEPIR sensor system. Results indicate an average 23.68% occupancy accuracy improvement when compared to the accuracy state delivered by individual SLEEPIR nodes. Results also indicate 7.74% occupancy accuracy improvement when compared to the accuracy state determined by an already proposed Particle Filter (PF) based occupancy estimation algorithm.