## 1 Introduction  
With new developments in science and technology, social participation rates among disabled individuals have steadily increased. For example, paraplegic patients now have access to enhanced mobility platforms with advances in exoskeleton robot technology [1, 2, 3].  
Safe and optimal operation of exoskeleton-type wearable robots by paraplegic patients requires that patients receive appropriate training. Here, we present the novel time-scalable posture detection algorithm (TSPDA) that can be used in exoskeleton training for paraplegic patients. TSPDA compares reference posture data (to be imitated by the patient) with posture data (recorded during training by the actual paraplegic patient) and extracts the evaluation target data required to measure the accuracy of the patient's recorded posture.  
Some paraplegic patients may find it difficult to follow the target posture at the beginning of rehabilitation, while some may respond more quickly than expected. Thus, when comparing reference posture data with a paraplegic patient's posture data, the posture data of a paraplegic patient undergoing rehabilitation training may differ greatly from reference data in terms of movement speed. For example, reference posture data could indicate that it takes 5 s (s) to walk five steps. However, a paraplegic patient following the reference posture data may actually take 10 s to walk five steps. In this case, an accurate comparison between paraplegic patients and reference posture data are only possible when the time scale is doubled. It is necessary to find a way to determine whether the paraplegic patient has performed the correct motion, even if the time scale is different.  
A posture comparison algorithm for the rehabilitation of paraplegic patients should flexibly adjust the time scale of reference data. The TSPDA introduced in this paper can overcome time-scale limitations; it can also allow comparison with other postures by changing the reference data.  
Here, we use full body motion capture equipment featuring an IMU sensor [4] for postural analysis. In the past, IMU sensors were unreliable. However, the technology has greatly improved--reliability is now assured [5; 6; 7].  
TSPDA compares the start and end frames of reference posture data with the complete data recorded from the paraplegic patient during training, and thereby avoid any time-scale constraints. The algorithm also utilizes the slice count method to reduce computational load: it limits the number of comparison samples between reference posture data and paraplegic patient data.  
Considering that the posture accuracy of a paraplegic patient undergoing rehabilitation activities may be relatively low initially, the TSPDA has a function that allows for a minimum target accuracy to be set to accommodate the detection of posture data as it changes with the patient's range of motion.  
The remainder of this paper is organized as follows. Section 2 describes related research. Section 3 presents the definitions of terms used in the algorithm and the algorithm's structure through diagrams and detailed module descriptions. Section 4 discusses an evaluation of the implemented algorithm and a performance analysis. Section 5 summarizes our findings and provides concluding remarks.