Objective To analyze the injury and dysfunction as well as the rehabilitation status and demand of the 188 Lushan earthquake victims admitted in the hospitals in and around Ya’an city and the West China Hospital of Sichuan University, so as to provide guidance for the rehabilitation work in the following step. Methods By means of the onsite investigation, 122 victims admitted in 7 hospitals and 2 health centers in and around Ya’an city within 1-9 days after Lushan earthquake, and the other 66 victims treated in the West China Hospital of Sichuan University were analyzed. Results The injury categorization of 122 victims in and around Ya’an city was as follows: upper limb fracture (12.30%), lower limb fracture (42.62%), spine fracture (16.39%) (25.00% treated by surgery and 75.00% treated by non-surgery treatment), pelvis fracture (1.64%), rib fracture (4.10%), traumatic brain injury (10.66%), soft tissue contusion (8.20%), and others (4.09%). At the corresponding period, the injury categorization of 66 victims treated in the department of rehabilitation medicine of the West China Hospital of Sichuan University was as follows: fracture (77.27%), traumatic brain injury (3.03%), spinal cord injury (4.55%), and others, including soft tissue injury (15.15%). At the ninth day after earthquake, among the 122 victims in and around Ya’an city, 8 victims (6.56%) were recovered with self-care ability of daily living, and the other 144 (93.44%) still needed the strengthened rehabilitation treatment. At the corresponding period, among the 66 victims in the West China Hospital, one victim (1.52%) was recovered with self-care ability of daily living, and the other 65 (98.48%) still needed the strengthened rehabilitation treatment. Conclusion Early rehabilitation treatment such as active exercise, elevating injured limbs, physical therapy, turning over at regular time, and psychological intervention can help the earthquake victims to return to home and society early.
This paper realized a portable brain-computer interface (BCI) system tailored for smart healthcare. Through the decoding of steady-state visual evoked potential (SSVEP), this system can rapidly and accurately identify the intentions of subjects, thereby meeting the practical demands of daily medical scenarios. Firstly, an SSVEP stimulation interface and an electroencephalogram (EEG) signal acquisition software were designed, which enable the system to execute multi-target and multi-task operations while also incorporating data visualization functionality. Secondly, the EEG signals recorded from the occipital region were decomposed into eight sub-frequency bands using filter bank canonical correlation analysis (FBCCA). Subsequently, the similarity between each sub-band signal and the reference signals was computed to achieve efficient SSVEP decoding. Finally, 15 subjects were recruited to participate in the online evaluation of the system. The experimental results indicated that in real-world scenarios, the system achieved an average accuracy of 85.19% in identifying the intentions of the subjects, and an information transfer rate (ITR) of 37.52 bit/min. This system was awarded third prize in the Visual BCI Innovation Application Development competition at the 2024 World Robot Contest, validating its effectiveness. In conclusion, this study has developed a portable, multifunctional SSVEP online decoding system, providing an effective approach for human-computer interaction in smart healthcare.