###### Abstract  
Monitoring forearm muscle contraction force in home-based rehabilitation remains challenging. Electromyography (EMG), as a standard technique, is considered impractical and complex for independent use by patients at home, which poses a risk of device misattachment and inaccurate recorded data. Considering the muscle-related modality, several studies have demonstrated an excellent correlation between stretch sensors and EMG, which provides significant potential for addressing the monitoring issue at home. Additionally, due to its flexible nature, it can be attached to the finger, which facilitates the logging of the kinematic mechanisms of a finger. This study proposes a method for estimating forearm muscle contraction in a cylinder grasping environment during home-based rehabilitation using a stretch-sensor glove. This study employed support vector machine (SVM), multi-layer perceptron (MLP), and random forest (RF) to construct the estimation model. The root mean square (RMS) of the EMG signal, representing the muscle contraction force, was collected from 10 participants as the target learning for the stretch-sensor glove. This study constructed an experimental design based on a home-based therapy protocol known as the graded repetitive arm supplementary program (GRASP). Six cylinders with varying diameters and weights were employed as the grasping object. The results demonstrated that the RF model achieved the lowest root mean square error (RMSE) score, which differed significantly from the SVM and MLP models. The time series waveform comparison revealed that the RF model yields a similar estimation output to the ground truth, which incorporates the contraction-relaxation phases and the muscle's contraction force. Additionally, despite the subjectivity of the participants' grasping power, the RF model could produce similar trends in the muscle contraction forces of several participants. Utilizing a stretch-sensor glove, the proposed method demonstrated great potential as an alternative modality for monitoring forearm muscle contraction force, thereby improving the practicality for patients to self-implement home-based rehabilitation. forarm; home-based rehabilitation; muscle contraction force; stretch-sensor glove licenses/by/4.0/. dysfunction can lead to irregularities in contraction force and movement, with forearm muscles playing a significant role in hand and finger movements. Untreated injuries may lead to permanent abnormalities. Rehabilitation is a standard treatment for muscle injuries, integrating medical, social, educational, and vocational interventions to restore functional abilities [4]. Recent research shows that home-based rehabilitation is effective, providing individuals better access and allowing more time for retraining [5]. Additionally, since the approach is both affordable and readily available, therapy can continue until the individual is satisfied with their ability to perform daily activities independently. One of the methods known for its safety, practicality, and affordability is the graded repetitive arm supplementary program (GRASP). GRASP consists of several exercises, one of which is the coordination function designed to enhance the affected hand's ability to perform daily activities using affordable and everyday tools, such as lifting or pouring with cylinder objects [6]. A key point during the program is to ensure that the exercises are challenging for the patients. Thus, several cylinder sizes and weight variations are applicable. However, monitoring muscle conditions during home-based therapy presents challenges. Monitoring the condition of the muscle, especially during functional activities such as cylindrical grasping, is essential to observe any existing abnormalities comprehensively. One viable method for understanding muscle condition is estimating the muscle contraction force [7].