###### Abstract  
Lower limb rehabilitation robot (LLRR) users, to successfully conduct isotonic exercises, require real-time feedback on the torque they exert on the robot to meet the goal of the treatment. Still, direct torque measuring is expensive, and indirect encoder-based estimation strategies, such as inverse dynamics (ID) and Nonlinear Disturbance Observers (NDO), are sensitive to Body Segment Inertial Parameters (BSIFs) uncertainties. We envision a way to minimize such parametric uncertainties. This paper proposes two human-robot interaction torque estimation methods: the Identified ID-based method (IID) and the Identified NDO-based method (INDO). Evaluating in simulation the proposal to apply, in each rehabilitation session, a sequential two-phase method: (1) An initial calibration phase will use an online parameter estimation to reduce sensitivity to BSIFs uncertainties. (2) The torque estimation phase uses the estimated parameters to obtain a better result. We conducted simulations under signal-to-noise ratio (SNR) = 40 dB and 20% BSIFs uncertainties. In addition, we compared the effectiveness with two of the best methods reported in the literature via simulation. Both proposed methods obtained the best Coefficient of Correlation, Mean Absolute Error, and Root Mean Squared Error compared to the benchmarks. Moreover, the IID and INDO fulfilled more than 72.2% and 88.9% of the requirements, respectively. In contrast, both methods reported in the literature only accomplish 27.8% and 33.3% of the requirements when using simulations under noise and BSIFs uncertainties. Therefore, this paper extends two methods reported in the literature and copes with BSIFs uncertainties without using additional sensors. exoskeletons; system identification; force feedback; computer simulation; mathematical model; nonlinear systems +
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2013, about 70 million people lived with disabilities in Latin America and the Caribbean, which is equivalent to a 12.5% of the regional population. The Pan American Health Organization (PAHO) reported that 6.3% of the people in Colombia had a state of disability, taking second place in Latin American countries after Brazil [5]. Additionally, 29% of those persons with disabilities have limitations to move or walk [6]. Multiple causes can trigger these types of limitations. In Colombia, one of the most disturbing causes is the high incidence of victims of antipersonnel mines (APM), improvised explosive devices (IED), and unexploded ordnance (UXO). There have been more than 11,700 APM and UXO victims since 1990. According to the Colombian National Development Plan 2018-2022, this situation is one of the most unfortunate consequences of the armed conflict in Colombia. These artifacts may have been activated for 20 years, and today, they are still hidden in several areas of the country, which makes it one of the main challenges to be faced in the post-conflict. Within the total of the victims, 20% died and the remaining 80% were injured, requiring physical rehabilitation during pre-prosthetic and prosthetic phases [7, 8]. There are many traditional methods for rehabilitation purposes. However, the study of new technologies applied in automation and bioengineering has brought research and experimentation in robotic platforms for lower limb rehabilitation [9]. A lower limb rehabilitation robot (LLRR) may be used to conduct isotonic exercises for victims of APM, IED, and UXO. According to [PERSON] et al. [10], an isotonic exercise is a type of resisted mode. A resistive method is an exercise where the patient is asked to overcome a given force during the movements imposed by the LLRR. Ideally, during isotonic exercises, the force of the subject is constant. In contrast, the muscle's length changes, i.e., the joint angle changes, and the velocity changes. Such ideal isotonic exercise may be satisfied by quantifying the interaction torque that the subject performs. This variable is also defined as human-robot interaction torque.