Wearable sensors (WS) are used in recent advances because researchers have successfully implemented body-worn devices to monitor personal locomotion behaviors and recognize human activity. The most common WS uses an accelerometer and gyroscope integrated into one wearable inertial mobile unit (IMU). Another type of WS based on the electrical current associated with muscular actions is also used in combination with IMUs for HAR [22]. These WS named electromyography (EMG) measure the myoelectric signals produced by muscular actions, hence their importance in activity recognition. A study on the fusion of EMG and IMU sensors for HAR is presented in [22], showing the potential of incorporating EMG signals in activity recognition.

A motoring real-time personal locomotion is introduced in [8], wielding three inertial sensors at different body locations (wrist, thigh, and chest). Data were processed through Gaussian and zero-phase filters. A hierarchical feature-based technique is used to extract features based on stochastic gradient descent optimization methods, achieving an accuracy rate of 92.50% in their experiments using the HuGaDB dataset. In [5], a technique to extract features using Discrete Fourier Transforms is proposed to estimate the mean power in selected frequency bands for ataxic gait assessment recognition. The accelerometric data were acquired by 31 time-synchronized sensors (perception neuron system) located at different body parts. Different classifiers were used for evaluation, such as support vector machines, Bayesian, nearest neighbors, and neural network methods, with the highest accuracy of 98.5%. The data comprised 13 normal and 12 ataxic individuals, and the entire study was conducted in a clinical environment. Deep learning techniques were applied to predict falls in older adults [9]. Data were collected on fall risk factors in the elderly using WS (accelerometers), questionaries, and physical tests. The dataset consisted of 296 older adults wearing a triaxial accelerometer on their lower back for a week and the following six months in which fall incidences and descriptions were obtained. Researchers used the raw accelerometer data (without making the preprocessing step) as input to an LSTM classifier, obtaining a time reduction and an AUC (Area Under the Curve) of 0.75.

In summary, most of the works conclude that the more sensors and extracted features from data, the better the accuracy of the computer classification algorithm. Nevertheless, sensors installed on the human body produce less comfort for the patient (ergonomic), make it more challenging to perform human activities, increase noise, and require more time to (pre/post)process data and analyze the activities in real time [29].