Sarcopenia is characterized by a persistent reduction in skeletal muscle mass and function, which, if undetected and untreated, can lead to serious consequences such as increased fall risk and long-term immobility. Although sarcopenia has gained attention in recent years, there are currently no effective pharmacological treatments. Muscle loss can only be mitigated in the early stages through resistance training and nutritional interventions. However, precise detection methods used in hospitals are often costly and inaccessible to the general population. Traditional screening methods, like the Sarcopenia Self-Screening Questionnaire (SARC-F), proposed by the Asian Working Group for Sarcopenia, rely on subjective patient reports and lack objectivity. This study aims to address this gap by developing a rapid and convenient sarcopenia screening device that can be used at home or in community settings. Leveraging Internet of Things (IoT) technology, we developed a surface electromyography (sEMG) system to measure the muscle activity of the gastrocnemius, tibialis anterior, and quadriceps during walking and standing. The muscle signals were wirelessly transmitted using the MQTT protocol for backend analysis, enabling real-time gait and muscle strength monitoring. Twenty-eight healthy elderly individuals from the community participated in this study. To validate the effectiveness of the device, traditional sarcopenia-related physical measurements were obtained using standard methods. We first classified participants into four sarcopenia risk levels using the above data. In the meantime, we used deep image gait analysis software (GaitBEST) to classify it into four risk levels. Preliminary results show that these two classification levels had a slight discrepancy. However, the sEMG signals obtained using the developed device reflect the risk level determined by gait pattern, indicating the necessity of further analysis to explore the relation between gait and skeletal muscle functions.