Low back pain (LBP) is the most common work-related musculoskeletal disorder among healthcare workers and is directly related to long hours of working in twisted/bent postures or with awkward trunk movements. It has already been established that providing relevant feedback helps individuals to maintain better body posture during the activities of daily living. With the goal of preventing LBP through objective monitoring of back posture, this paper proposes a wireless, comfortable, and compact textile-based wearable platform to track trunk movements when the user bends forward. The smart garment developed for this purpose was prototyped with an inductive sensor formed by sewing a copper wire into an elastic fabric in a zigzag pattern. The results of an extensive simulation study showed that this unique design increases the inductance value of the sensor, and, consequently, improves its resolution. Furthermore, experimental evaluation on a healthy participant confirmed that the proposed wearable system with the suggested sensor design can easily detect forward bending movements. The evaluation scenario was then extended to also include twisting and lateral bending of the trunk, and it was observed that the proposed design can successfully discriminate such movements from forward bending of the trunk. Results of the magnetic interference test showed that, most notably, moving a cellphone towards the unworn prototype affects sensor readings, however, manipulating a cellphone, when wearing the prototype, did not affect the capability of the sensor in detecting forward bends. The proposed platform is a promising step toward developing wearable systems to monitor back posture in order to prevent or treat LBP.

(1) Background: Quantitative evaluation of gait parameters can provide useful information for constructing individuals’ gait profile, diagnosing gait abnormalities, and better planning of rehabilitation schemes to restore normal gait pattern. Objective determination of gait phases in a gait cycle is a key requirement in gait analysis applications; (2) Methods: In this study, the feasibility of using a force myography-based technique for a wearable gait phase detection system is explored. In this regard, a force myography band is developed and tested with nine participants walking on a treadmill. The collected force myography data are first examined sample-by-sample and classified into four phases using Linear Discriminant Analysis. The gait phase events are then detected from these classified samples using a set of supervisory rules; (3) Results: The results show that the force myography band can correctly detect more than 99.9% of gait phases with zero insertions and only four deletions over 12,965 gait phase segments. The average temporal error of gait phase detection is 55.2 ms, which translates into 2.1% error with respect to the corresponding labelled stride duration; (4) Conclusions: This proof-of-concept study demonstrates the feasibility of force myography techniques as viable solutions in developing wearable gait phase detection systems.

Prostheses provide a means for individuals with amputations to regain some of the lost functions of their amputated limb. Human-machine interfaces (HMIs), used for controlling prosthetic devices, play a critical role in users' experiences with prostheses. This review article provides an overview of the HMIs commonly adopted for upper-limb prosthesis control and inspects collected signals and their processing methods.

Developing wearable platforms for unconstrained monitoring of limb movements has been an active recent topic of research due to potential applications such as clinical and athletic performance evaluation. However, practicality of these platforms might be affected by the dynamic and complexity of movements as well as characteristics of the surrounding environment. This paper addresses such issues by proposing a novel method for obtaining kinematic information of joints using a custom-designed wearable platform. The proposed method uses data from two gyroscopes and an array of textile stretch sensors to accurately track three-dimensional movements, including extension, flexion, and rotation, of a joint. More specifically, gyroscopes provide angular velocity data of two sides of a joint, while their relative orientation is estimated by a machine learning algorithm. An unscented Kalman filter (UKF) algorithm is applied to directly fuse angular velocity/relative orientation data and estimate the kinematic orientation of the joint. Experimental evaluations were carried out using data from 10 volunteers performing a series of predefined as well as unconstrained random three-dimensional trunk movements. Results show that the proposed sensor setup and the UKF-based data fusion algorithm can accurately estimate the orientation of the trunk relative to pelvis with an average error of less than 1.72 degrees in predefined movements and a comparable accuracy of 3.00 degrees in random movements. Moreover, the proposed platform is easy to setup, does not restrict body motion, and is not affected by environmental disturbances. This study is a further step towards developing user-friendly wearable sensor systems than can be readily used in indoor and outdoor settings without requiring bulky equipment or a tedious calibration phase.

Background: Impaired arm position sense is a common somatosensory impairment after stroke, which significantly impacts the performance of functional activities using the upper limb. However, few clinical interventions target loss of position sense after stroke. Our aim was to use interlimb force-coupling to augment position sense of the stroke-affected arm during a bilateral reaching task and investigate the impact of training with this feedback manipulation on measures of arm position matching ability and both bilateral and unilateral motor control. Methods: Twenty-four participants with a history of stroke were randomized ( N = 12/group) to perform mirrored bimanual aiming movements with either interlimb force-coupling (Augmented PF) or uncoupled symmetrical reaches with only visual feedback about movement position. Participants completed 11 sessions (295 bimanual reaches/session) using a Kinarm End-Point robot. Performance on measures of arm position sense (Arm Position Matching, APM), motor impairment (Fugl-Meyer Upper Limb, FM), motor function (Wolf Motor Function Test, WMFT), unilateral reach accuracy and speed (Visually Guided Reaching, VGR), and bilateral reach symmetry were collected before and after training to characterize changes in upper limb somatosensory and motor control performance. Results: APM Task Scores improved for both groups. This improvement was specifically observed through reduced APM variability, but not accuracy. FM scores also improved for both groups. The group that did not practice with force-coupling between limbs improved on measures of bilateral movement symmetry on a mirrored reaching task and had faster VGR movement times in post-test. Conclusion: Symmetrical reach training with or without augmented PF led to reduced motor impairment and benefited upper limb position matching ability by reducing APM variability. Augmenting position sense during reaching did not provide additional benefits for position matching accuracy. Advantages for unilateral movement speed and bilateral reach symmetry measures in the group that practiced without interlimb coupling may reflect specificity of practice effects due to similarity between test and training conditions for this group.

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