Analyzing how we walk in our home can provide valuable information about locomotor efficiency and general well-being. Through the shape of our trajectories, basic walking metrics such as distance, time, and speed, and space occupancy characteristics, human locomotor behaviours at home, and the way they convey markers of wellness, can be investigated. The objective of this study was to detect, reconstruct and identify locomotor trajectories during walking at home with a capacitive sensing device, the SensFloor, recently installed in an apartment located in the centre of Montpellier, in the South of France. Data were collected both in controlled scenarios - linear and curvilinear trajectories repetitively performed by three experimental participants - and during a full day of real occupancy by one inhabitant. The algorithmic approach followed (i) identification of static graph, spatial and temporal neighbourhood from floor activation, (ii) data filtering, (iii) trajectory reconstruction through chains of dynamical nodes, and (iv) identification of walking metrics, such as locomotor distance, time, and velocity. Results revealed the efficacy of the algorithm to successfully capture human trajectories inside the apartment under different conditions, including when curvilinear trajectories are performed simultaneously by two participants in different rooms. Applied to a full random day of occupancy by a single inhabitant, our algorithm also revealed the existence of 47 individual locomotor trajectories, calculated out of 129 thousand recorded floor contacts, yielding an average walking velocity μ= 1.1 m/s with σ = 0.3 m/s, an average walking distance μ = 9.4 m and an average walking duration of μ = 8.8 s. The success of our algorithm to detect locomotor trajectories now allow the exploration of walking patterns at home and their correspondence to well-being. Acknowledgments: This research has been conducted as part of the Human at home projecT (HUT) co-financed by the European Regional Development Fund (ERDF) and the Occitanie Region. This acknowledgment extends to the support given by Montpellier Mediterranee Metropole.