Traditional methods to assess balance often suffer from subjectivity, aren't comprehensive enough and can't be administered remotely. They also are expensive and require specialized equipment and clinical expertise. Using wearable sensors and advanced machine learning algorithms, researchers offer a practical and cost-effective solution for capturing detailed movement data, essential for balance analysis. This approach is more accessible and can be administered remotely, which could have significant implications for health care, rehabilitation, sports science or other fields where balance assessment is important.
