SensePresence: Infrastructure-Less Occupancy Detection for Opportunistic Sensing Applications

Abstract

Predicting the occupancy related information in an environment has been investigated to satisfy the myriad requirements of various evolving pervasive, ubiquitous, opportunistic and participatory sensing applications. Infrastructure and ambient sensors based techniques have been leveraged largely to determine the occupancy of an environment incurring a significant deployment and retrofitting costs. In this paper, we advocate an infrastructure-less zero-configuration multimodal smartphone sensor-based techniques to detect fine-grained occupancy information. We propose to exploit opportunistically smartphones' acoustic sensors in presence of human conversation and motion sensors in absence of any conversational data. We develop a novel speaker estimation algorithm based on unsupervised clustering of overlapped and non-overlapped conversational data to determine the number of occupants in a crowded environment. We also design a hybrid approach combining acoustic sensing opportunistically with locomotive model to further improve the occupancy detection accuracy. We evaluate our algorithms in different contexts; conversational, silence and mixed in presence of 10 domestic users. Our experimental results on real-life data traces collected from 10 occupants in natural setting show that using this hybrid approach we can achieve approximately 0.76 error count distance for occupancy detection accuracy on average.

Wearable Sensor-Based Location-Specific Occupancy Detection in Smart Environments

“…Crowd++ [6] was proposed to combine pitch with MFCC to compute the number of people with an average error distance of 1.5 speakers. On the other hand, our MFCC-based proposed model improved the average error distance by a factor of two (0.76 speakers) [13,14]. However, the major disadvantage of the MFCC-based acoustic approach is that the MFCC discards a lot of information present in the speech sound.…”

“…In particular, we propose a zero-hassle ambient and infrastructure-less mobile sensing (a.k.a. smartphone) based approach by exploiting only the smartphone's sensors to provide significantly greater visibility on real-time occupancy and its semantic location [13,14]. e key challenge in this case is to effectively estimate the number of people in a crowded and noncrowded environment either in the presence of any conversational data or not.…”

Crowd Size Estimation Using CommSense Instrument for COVID-19 Echo Period

“…In addition, for the post-COVID scenario, we need crowd size estimation on a smaller scale. The work in [18] uses smartphones' acoustic sensors in the presence of human conversation, and motion sensors in the absence of any conversational data, for crowd size estimation. The presence of up to ten occupants was tested with this proposed system.…”

AOCM-OAC: Architecture of Optimal Computational Model for Occupant Action Classification using Machine Learning

“…The work presented by Jin et al [38] have used sharing concept of the environmental factors in order to perform detection of the occupancy. Khan et al [39] have presented a hybrid approach in order to enhance the accuracy of the detection process. Adoption of deep sensing approach has been witnessed in the work of Li et al [40] where stochastic approach as well as state-based approach has been used for performing spectrum sensing.…”