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Wireless Body Sensor Networks (WBSNs) have proved to be a suitable technology for supporting the monitoring of physical and physiological activities of the human body. However, avoiding erroneous behavior of WBSN-based systems is an issue of fundamental importance, especially for critical health-care applications. In this regard, proper self-healing techniques should be able to fulfill requirements such as fault tolerance and reliability by detecting, and possibly recovering, faults and errors at runtime. In this paper, we focus on data faults, by first studying the impact of corrupted data, affecting sensed data by different kind of data-fault models, on the accuracy of a human activity recognition system. Then, we describe how the SPINE-* framework is able to enhance the WBSN system by adding instrumental autonomic elements providing the necessary self-healing operations. We find that the use of autonomic elements makes the system much more efficient and reliable thanks to its improved tolerance to data faults, as demonstrated by experimental results. [ABSTRACT FROM PUBLISHER]