Unravelling the gait and balance: A novel approach for detecting depression in young healthy individuals

Article type: Research Article

Authors: Maguluri, Lakshmana Phaneendra^{a; *} | Vinya, Viyyapu Lokeshwari^b | Goutham, V.^c | Uma Maheswari, B.^d | Kumar, Boddepalli Kiran^e | Musthafa, Syed^f | Manikandan, S.^g | Srivastava, Suraj^h | Munjal, Nehaⁱ

Affiliations: [a] Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur Andhra Pradesh, India | [b] Department of Computer Science andEngineering, Vardhaman College of Engineering, Shamshabad, Telangana, India | [c] Department of Computer Science and Engineering, St. Mary’s group of Institutions, Hyderabad Telangana, India | [d] Department of Computer Science and Engineering, St. Joseph’s College of Engineering, OMR- Chennai, India | [e] Department of Computer Science and Engineering, Aditya College of Engineering, Surampalem, Andhra Pradesh, India | [f] Department of Information Technology, M.Kumarasamy College of Engineering, Karur, Tamil Nadu, India | [g] Department of Electrical and Electronics Engineering, Karpagam Institute of Technology, Coimbatore, Tamil Nadu, India | [h] Department of Computer science and Engineering, I.K.G.PTU Mohali Campus-I, Sahibzada Ajit Singh Nagar, Punjab, India | [i] Department of Physics, Lovely Professional University, Phagwara, Punjab, India

Correspondence: [*] Corresponding author. Lakshmana Phaneendra Maguluri, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur Andhra Pradesh 522502, India. E-mail: lakshmanaphaneendra@gmail.com.

Abstract: Depression is a prevalent mental health disorder that affects people of all ages and origins; therefore, early detection is essential for timely intervention and support. This investigation proposes a novel method for detecting melancholy in young, healthy individuals by analysing their gait and balance patterns. In order to accomplish this, a comprehensive system is designed that incorporates cutting-edge technologies such as a Barometric Pressure Sensor, Beck Depression Inventory (BDI), and t-Distributed Stochastic Neighbour Embedding (t-SNE) algorithm. The system intends to capitalize on the subtle motor and physiological changes associated with melancholy, which may manifest in a person’s gait and balance. The Barometric Pressure Sensor is used to estimate variations in altitude and vertical velocity, thereby adding context to the evaluation. The mood states of participants are evaluated using the BDI, a well-established psychological assessment instrument that provides insight into their emotional health. Integrated and pre-processed data from the Barometric Pressure Sensor, BDI responses, and gait and balance measurements. The t-SNE algorithm is then used to map the high-dimensional data into a lower-dimensional space while maintaining the local structure and identifying underlying patterns within the dataset. The t-SNE algorithm improves visualization and pattern recognition by reducing the dimensionality of the data, allowing for a more nuanced analysis of depression-related markers. As the proposed system combines objective physiological measurements with subjective psychological assessments, it has the potential to advance the early detection and prediction of depression in young, healthy individuals. The results of this exploratory study have implications for the development of non-intrusive and easily accessible instruments that can assist healthcare professionals in identifying individuals at risk and implementing targeted interventions.

Keywords: Depression, barometric pressure sensor, beck depression inventory, t-SNE, mental health

DOI: 10.3233/JIFS-235058

Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12079-12093, 2023