Extracting Walking Trajectories at Home From a Capacitive Proximity Sensing Floor

Sannier, M.; Janaqi, Stefan; Raducanu, Vinicius; Barysheva, V.; Haddou, Hassan Ait; Pla, Simon; Dray, G.; Bardy, Benoît G.

doi:10.1109/jsen.2021.3139442

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…The idea of this work is to track the footstep on the oor using capacitive sensing. Similar form of study was also formulated by Sannier et al [33] where a trajectory of walking over the oor is monitored using capacitive sensing. The model can estimate behavior for both spatial and temporal feature.…”

Section: Introductionmentioning

confidence: 92%

Novel Framework for Multi-Scale Occupancy Sensing for distributed monitoring in Internet-of-Things

Mane,

Rao

2023

Preprint

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Occupancy sensing is one of the integral parts of modern evolving security surveillance and monitoring system used over different types of infrastructure. With an aid of multiple form of occupancy sensors, the prime idea of occupancy sensing is to identify the presence or absence of occupants in specifically monitored area followed by transmitting back the sensing information either for storage or for prompting a set of commands from the connected control units. Review of existing schemes exhibits the presence of adoption of multiple methodologies over different variants of use-cases; however, they are quite case specific, uses expensive deployment process, and performs highly sophisticated operation. At present, there are no studies specifically reported of using multi-scale occupancy sensing suitable for large and distributed environment of Internet-of-Things (IoT). Therefore, the proposed study introduces a mechanism of novel multi-scale occupancy sensing considering a use case of smart university campus, although, it can be implemented over any form of different infrastructures too connected over IoT environment. The proposed scheme is implemented considering different types of cost-effective sensors, handheld devices and access points in order to identify the state of occupancy in large number of rooms present in the campus. The sensed data from distributed connected campus are aggregated over cloud server where they are subjected to suitable preprocessing to increase the data quality suitable for reliable prediction. Multiple set of potential learning-based schemes are integrated with proposed model to explore best fit model. This assessment scenario is not found reported in existing scheme to perform classification of states of occupancy. The study outcome shows Convolution Neural Network and Long Short-Term Memory to accomplish higher accuracy compared to other learning approach.

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Section: Introductionmentioning

confidence: 92%

Novel Framework for Multi-Scale Occupancy Sensing for distributed monitoring in Internet-of-Things

Mane,

Rao

2023

Preprint

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“…However, the investigation of how we walk in the restricted space of our everyday living environment, i.e., our home, is a relatively recent area of study. Research has begun to document the behavior of healthy individuals at home (Glaister et al, 2007;WSJ, 2012;Sannier et al, 2022), with implications for rehabilitation (Lim et al, 2021) and healthy aging (Taleb et al, 2009;Borelli et al, 2019). However, crucial information is still lacking, particularly in understanding the characteristics, needs, and locomotor constraints of people living naturally at home.…”

Section: Introductionmentioning

confidence: 99%

Obstacles shape the way we walk at home

Sannier,

Janaqi,

Dray

et al. 2023

Front. Comput. Sci.

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IntroductionWalking indoors, particularly at home, presents a distinct experience compared to the conventional pedestrian walking classically described. Our homes encompass intricate, confined, and cluttered architectural spaces that necessitate a predominantly curvilinear walking pattern. Despite the growing interest in studying our home, spurred by successive COVID-19 lockdowns, there remains a dearth of information regarding our walking behaviors inside homes, yet rich in data on the physical and sensory links between humans and their daily interior environment.MethodsThis study presents the outcomes of a controlled experiment conducted in an apartment in Montpellier, France. Participants were tasked with traversing the living room at a natural pace, encountering two natural obstacles-a large dining table and a small coffee table. They then walked back in opposite direction, circumnavigating the same two obstacles. To examine walking behavior within a pseudo-natural context, three conditions were tested: a controlled condition and two conditions that perturbed the natural curvilinear trajectory perceptually, by imposing an unpleasant sound, or physically, by suddenly displacing the coffee table between conditions. Twenty participants performed 30 trials in each condition. We approximated the position of their center of mass and computed various metrics related to their trajectories, including walking speed, obstacle clearance distance, its adaptation over time, and inter-trial trajectory variability.ResultsFindings revealed a greater visual clearance distance for the dining table compared to the coffee table, a difference reduced by the perturbation caused by displacing the coffee table. This clearing distance diminished with repetitions, showing that over time we tend to walk closer to obstacles around us. These adaptations were clearly the result of an active visuo-motor regulation, as evidenced by the reduced trajectory variability at, or just before, the location of the obstacles.DiscussionCollectively, these results demonstrate that walking at home is a flexible behavior necessitating continuous perceptual adaptations in our daily trajectories. These findings could contribute to a detailed analysis of walking indoors under natural conditions, and the investigated metrics could serve as a baseline for comparing the embodiment of physical and mental health in walking patterns, for instance during lockdowns. Furthermore, our findings have consequences for safer mediated human architecture interaction.

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Novel Framework for Multi-Scale Occupancy Sensing for Distributed Monitoring in Internet-of-Things

Mane,

Rao

2024

Wireless Pers Commun

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Occupancy sensing is one of the integral parts of modern evolving security surveillance and monitoring system used over different types of infrastructure. With an aid of multiple form of occupancy sensors, the prime idea of occupancy sensing is to identify the presence or absence of occupants in speci cally monitored area followed by transmitting back the sensing information either for storage or for prompting a set of commands from the connected control units. Review of existing schemes exhibits the presence of adoption of multiple methodologies over different variants of use-cases; however, they are quite case speci c, uses expensive deployment process, and performs highly sophisticated operation. At present, there are no studies speci cally reported of using multi-scale occupancy sensing suitable for large and distributed environment of Internet-of-Things (IoT). Therefore, the proposed study introduces a mechanism of novel multi-scale occupancy sensing considering a use case of smart university campus, although, it can be implemented over any form of different infrastructures too connected over IoT environment. The proposed scheme is implemented considering different types of cost-effective sensors, handheld devices and access points in order to identify the state of occupancy in large number of rooms present in the campus. The sensed data from distributed connected campus are aggregated over cloud server where they are subjected to suitable preprocessing to increase the data quality suitable for reliable prediction. Multiple set of potential learning-based schemes are integrated with proposed model to explore best t model. This assessment scenario is not found reported in existing scheme to perform classi cation of states of occupancy. The study outcome shows Convolution Neural Network and Long Short-Term Memory to accomplish higher accuracy compared to other learning approach.

show abstract

Extracting Walking Trajectories at Home From a Capacitive Proximity Sensing Floor

Cited by 5 publications

References 15 publications

Novel Framework for Multi-Scale Occupancy Sensing for distributed monitoring in Internet-of-Things

Novel Framework for Multi-Scale Occupancy Sensing for distributed monitoring in Internet-of-Things

Obstacles shape the way we walk at home

Novel Framework for Multi-Scale Occupancy Sensing for Distributed Monitoring in Internet-of-Things

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