Sitting, standing, and walking are fundamental activities crucial for maintaining independence in daily life. However, aging or lower limb injuries can impede these activities, posing obstacles to individuals' autonomy. In response to this challenge, we developed the LM-Ease (lower-limb movement ease), a compact and soft wearable robot designed to provide hip assistance. Its purpose is to aid users in carrying out essential daily activities such as sitting, standing, and walking. The LM-Ease features a fully actuated tendon-driven system that seamlessly transitions between assistance actuation profiles tailored for sitting, standing, and walking movements. This device provides the user with gravity support during stand-to-sit, and offers hip extension assistance pulling force during sit-to-stand and walking. Our preliminary results show that with the LM-Ease, healthy young adults ( n $=$ 8) had significantly lower muscle activation: average reduction of 15.6% during stand-to-sit and 17.8% during sit-to-stand. Furthermore, with LM-Ease, participants demonstrated a 12.7% reduction in metabolic cost during ground walking. These evidences suggest that the LM-Ease holds potential in reducing muscular activation and energy expenditure during these fundamental daily activities. It could serve as a valuable tool for individuals seeking assistance in enhancing lower limb mobility, thereby bolstering their independence and overall quality of life.

Real-time continuous locomotion mode recognition and seamless timely transition detection is critical for the exoskeleton robot. This study aims to present a comprehensive and innovative framework for locomotion mode recognition and transition prediction, exclusively utilizing inertial measurement unit (IMU) signals from the exoskeleton. In this framework, a CNN-BiLSTM model was developed and trained to be the classifier and a novel majority filter was designed to reduce the transition misjudgment rate. Moreover, a comprehensive evaluation system encompassing eight dimensions for the classifier, incorporating evaluation metrics specifically for transition misjudgment, was proposed. We collected locomotion motion data from six subjects wearing a rigid exoskeleton robot using six IMU sensors on the exoskeleton. The proposed method achieves a high level of recognition accuracy, with an overall average of 99.58% for the five steady locomotion modes (level ground walking (LG), stair ascent/descent (SA/SD), and ramp ascent/descent (RA/RD)) across six subjects following the transition decision. All transitions are recognizable, and the majority can be predicted in advance, with an average prediction time of 353ms. Furthermore, the implementation of majority filter resulted in an average 87.04% reduction in the transition misjudgment rate among six subjects, thereby decreasing the average transition misjudgment rate to 4.82%. Finally, the model was tested on a Jetson Nano to verify its real-time performance. The results presented above were obtained under the condition where either leg could function as the first transition leg and revealed that the developed system was capable of achieving precise locomotion mode recognition and timely transition prediction, with high real-time performance.