The motion activities performed by a mobile or wearable device user are characteristic of the user and the accuracy of detection of motion activity context can be improved by incorporating personalized samples of motion activity. But obtaining personalized samples of motion activity from device user is not practically feasible. In this article, a semi-supervised method is presented for improving the classification accuracy of the mobile or wearable device user’s motion activities by deriving a classifier that is personalized to the user starting from a factory-set generalized classifier. The novelty lies in using information theoretic criterion to select personalized data samples from all the personalized data samples collected from the target user. This is useful in improving the motion activity detection accuracy, since each device user has a distinct gait personality as compared to the motion activity data patterns available in a generalized database that are used to train a generalized classifier. The average accuracy for 11 target users of a generalized classifier is 93.11% that increases to 96.50% using the proposed method when the device is used in in-hand mode. It is observed that the accuracy improvement with a personalized classifier over a generalized classifier is greater for those subjects whose accuracy is lower than others with the generalized classifier.

中文翻译：

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具有半监督学习的人体运动活动的个性化分类器

移动或可穿戴设备用户执行的运动活动是用户的特征，通过结合个性化的运动活动样本可以提高运动活动上下文检测的准确性。但是从设备用户那里获取个性化的运动活动样本实际上并不可行。在本文中，提出了一种半监督方法，通过从工厂设置的广义分类器开始推导出针对用户个性化的分类器，提高移动或可穿戴设备用户运动活动的分类精度。新颖之处在于使用信息论准则从目标用户收集的所有个性化数据样本中选择个性化数据样本。这有助于提高运动活动检测的准确性，因为与用于训练通用分类器的通用数据库中可用的运动活动数据模式相比，每个设备用户都有不同的步态个性。广义分类器的 11 个目标用户的平均准确率为 93.11%，当设备在手持模式下使用时，使用所提出的方法增加到 96.50%。可以观察到，对于那些准确性低于使用广义分类器的其他对象的对象，使用个性化分类器的准确性比广义分类器的准确性提高更大。当设备在手持模式下使用时，50% 使用建议的方法。可以观察到，对于那些准确性低于使用广义分类器的其他对象的对象，使用个性化分类器的准确性比广义分类器的准确性提高更大。当设备在手持模式下使用时，50% 使用建议的方法。可以观察到，对于那些准确性低于使用广义分类器的其他对象的对象，使用个性化分类器的准确性比广义分类器的准确性提高更大。