# Smartphone Accelerometer for Gait Assessment: Validity and Reliability in Healthy Adults
## 1 Introduction  
Human gait analysis plays a crucial role in healthcare, biometrics, and neurophysiology, enabling clinicians and researchers to monitor health, track disease progression, and aid in rehabilitation for conditions such as Parkinson's disease, stroke, and orthopedic surgeries [1]. Advanced technologies, such as machine learning, have further enhanced gait analysis by identifying unique walking patterns, making it a powerful tool for biometric recognition and security [2]. The application of statistical physics to gait analysis also offers insights into how gait reflects both physical and neurological disorders, contributing to a deeper understanding of human motor control [3]. Recent studies underscore the growing importance of gait analysis across various fields. One study demonstrated the application of Ultra-Wide Band (UWB) radar for gender classification, achieving high accuracy through machine learning models such as Multilayer Perceptron and Support Vector Machine, which are particularly useful in biometrics and surveillance [4]. Another review highlighted long-term gait impairments in patients following total knee arthroplasty, emphasizing the need for better rehabilitation methods [5]. Future trends in gait analysis will involve the integration of multiple sensing modalities with AI, which is expected to enhance accuracy and expand its applications in both clinical and wearable technology contexts [6]. Wearable devices are transforming gait analysis by enabling continuous, real-time monitoring in clinical, rehabilitation, and community settings. Inertial measurement units (IMUs) embedded in wearables provide precise data on joint kinematics, facilitating gait analysis outside traditional laboratory environments. Advances in sensor miniaturization and the development of flexible pressure sensors have made gait analysis more accessible in daily life, while 5G-enabled flexible antennas further enhance real-time data transmission [7; 8; 9]. These technologies are making gait analysis more convenient, less intrusive, and increasingly accurate for clinical use, providing reassurance to both clinicians and patients. In clinical applications, wearable devices play a significant role in the diagnosis and management of neurodegenerative diseases and movement disorders. For example, in [PERSON]'s disease, wearable systems equipped with force-sensitive insoles and IMUs continuously monitor abnormal gait patterns, providing real-time feedback to clinicians and assisting in early diagnosis [10]. These devices also significantly improve rehabilitation outcomes by enabling real-world gait assessments during post-surgical recovery, offering hope for better recovery experiences, and helping to detect subtle gait changes in conditions like Alzheimer's disease [8; 11]. Overall, wearables provide real-time data that optimize treatment strategies, making gait analysis more accessible in both clinical and home settings. Smartphone-based accelerometers provide a promising, cost-effective alternative to traditional gait analysis methods, such as optical motion capture and pressure-sensitive walkways. These systems have demonstrated accuracy and reliability in capturing kinematic gait parameters, making them viable for use in clinical and non-clinical environments [12]. Studies have confirmed their validity in measuring parameters such as step time, walking speed, and step length under various conditions, including treadmill walking and different head-turning scenarios [13; 14]. Advanced algorithms, such as feed-forward neural networks and signal segmentation techniques, enhance data quality by adapting to gait speed and individual variations [15]. Despite these advancements, challenges persist in measuring complex metrics, such as step asymmetry and nonlinear dynamics. Moreover, factors such as demographic attributes (e.g., sex and height) influence the outcomes, emphasizing the need for algorithmic customization.