# New Framework for Human Activity Recognition for Wearable Gait Rehabilitation Systems
## 1 Introduction
An eight-channel wireless sEMG (ZeroWirePlus) was used. Their signals were band-pass filtered at 10-400 Hz and sampled at 800 Hz, 960 Hz and 1000 Hz. They were applied on the following muscles: tibialis anterior (TA), gastrocnemius medialis (GM), soleus (SO), rectus femoris (RF), peroneus longus (PL), vastus medialis (VM), gluteus maximus (GMax) and biceps femoris (BF). Additional utilized sensors were a 9-camera motion capture system (SMART system) and two force plates (Kistler). The performed activities were walking at different speeds, toe-walking (T), heel-walking (H), step ascending (U) and step descending (D). Another dataset was presented by [PERSON] et al. (2019) with 50 healthy subjects. There were 26 males and 24 females. Their age range, height and weight were 37.0 \(\pm\) 13.6 years, 1.74 \(\pm\) 0.09 m, and 71.0 \(\pm\) 12.3 kg, respectively. A 10-camera optoelectronic system (OQU84, Qualisys) is used for data acquisition, and the data were sampled at 100 Hz. Ground forces and moments were recorded using two force plates (OR6-5-AMTI). Their data were sampled at 1500 Hz. Eight wireless versions of sEMG (Desktop DTS--Noraxon) were used to collect muscle data from the right leg, and the utilized muscles were gluteus maximus, gluteus medius, vastus medialis, rectus femoris, gastrocnemius medialis, semitendinosus, soleus, and tibialis anterior. Bandpass filtering was applied to these data between 30 and 300 Hz. During a single session, the following exercises were carried out: walking at five different speeds on a level, straight walkway. These speeds were 0-0.4 m. s\({}^{-1}\), 0.4-0.8 m. s\({}^{-1}\), and 0.8-1.2 m. s\({}^{-1}\) in addition to other faster speeds. In total, 1143 trials were completed for all subjects and all activities. From previous literature, it was proven the necessity of developing data classification and signal conditioning techniques and testing them on open-source datasets. In this way, new algorithms can be tested on previously validated data. Consequently, the new techniques can be applied to new experimental datasets and to human subjects. This paper presents a new methodology for the classification of ADLs using sEMG and IMUs with the intention of achieving high accuracy and low-speed classification. In this work, our objective is to present a novel autocepstrum-based framework for studying lower limb locomotion. The remarkable characteristic of autocepstrum analysis is enhancing the significant features representing a specific activity while suppressing noise such as additive Gaussian noise according to homomorphic filtering capabilities. The proposed work captures and extracts information from different lower limb muscles to accurately recognize human movement. Indeed, many transfemoral amputees have had their lower limbs removed entirely below the knee because of illness or an accident; our proposed work is greatly inspired by this fact. An open-source dataset has been selected for testing the proposed approach to reduce the complexity of hardware preparations and facilitate algorithm testing. From our observation, wearing many sEMG or IMU sensors may make the wearer uncomfortable and require a lot of data processing and hinder the portability of assistive devices. Hence, we aim to choose between the employment of sEMG and IMU based on the obtained classification accuracy. To ensure recognition accuracy, the number of sensors must be kept to a minimum. This can be fulfilled by deciding on the muscles that have the most effective contribution in identifying a specific activity. The methodology of this work is presented in Section 2. Different signal conditioning techniques are presented and applied to IMU and EMG signals in Section 3. The main proposed framework for activity recognition is shown in Section 4. The obtained results and their discussion and analysis are presented in Section 5. The paper is finalized with the conclusion and future work in Section 6.