A recently designed gait phase detection (GPD) system, with the ability to detect all seven phases of gait in healthy adults, was modified for GPD in children with cerebral palsy (CP). A shank-attached gyroscope sent angular velocity to a rule-based algorithm in LabVIEW to identify the distinct characteristics of the signal. Seven typically developing children (TD) and five children with CP were asked to walk on treadmill at their self-selected speed while using this system. Using only shank angular velocity, all seven phases of gait (Loading Response, Mid-Stance, Terminal Stance, Pre-Swing, Initial Swing, Mid-Swing and Terminal Swing) were reliably detected in real time. System performance was validated against two established GPD methods: (1) force-sensing resistors (GPD-FSR) (for typically developing children) and (2) motion capture (GPD-MoCap) (for both typically developing children and children with CP). The system detected over 99% of the phases identified by GPD-FSR and GPD-MoCap. Absolute values of average gait phase onset detection deviations relative to GPD-MoCap were less than 100 ms for both TD children and children with CP. The newly designed system, with minimized sensor setup and low processing burden, is cosmetic and economical, making it a viable solution for real-time stand-alone and portable applications such as triggering functional electrical stimulation (FES) in rehabilitation systems. This paper verifies the applicability of the GPD system to identify specific gait events for triggering FES to enhance gait in children with CP.

A new gyroscope-based gait phase detection system (GPDS) with ability to detect all seven phases of gait was proposed in this study. Gyroscopes were attached to each shank. Shank angular velocity, about the medio-lateral axis, was streamed to a PC and a rule-based algorithm was used to identify characteristics of the signals. Five subjects were asked to walk on treadmill at their self-selected speed while using this system. All 7 phases of gait: LR, MSt, TSt, PSw, ISw, MSw, and TSw were detected in real-time using only shank angular velocities. To quantify system performance, sensor data was compared to simultaneously collected motion capture data. Average gait phase detection delays of the system were less than 40ms except TSw (74ms). The present system, consisting of minimal sensors and decreased processing, is precise, cosmetic, economical, and a good alternative for portable stand-alone applications.

Background and Purpose Cerebral palsy (CP) is characterized by decreased passive joint range-of-motion and impaired walking, resulting in progressive loss of function. Typical gait training interventions for children with CP appear insufficient to mitigate these effects. The purpose of this case report is to describe the use of a new treadmill-based gait training intervention using active correction with functional electrical stimulation (FES) in 2 adolescents with CP. Case Description Two participants with CP (13-year-old girls, Gross Motor Function Classification System [GMFCS] level II and III) trained by walking on a treadmill, with FES assistance, for 30 minutes, 3 times per week, for 12 weeks. The intervention used a feedback control system to detect all 7 phases of gait in real time and triggered FES to the appropriate muscle groups (up to 5 bilaterally) based on the detected gait phase. Joint kinematics, step width, stride length, walking endurance, peak oxygen uptake ($\dot{v}^{o}_{2}$), and oxygen (O2) cost of walking were evaluated preintervention and postintervention. Outcomes Both participants showed improved knee and ankle angles and step width relative to children who are typically developing, and both exhibited increased stride length. One participant (GMFCS III) improved peak $\dot{v}^{o}_{2}$and walking endurance but not O2 cost of walking at her original self-selected walking speed. The other participant (GMFCS II) improved O2 cost of walking but not peak $\dot{v}^{o}_{2}$ or walking endurance. These differences are partly explained by differences in gait type, functional abilities, and initial fitness levels. Most improvements persisted at follow-up, indicating short-term neurotherapeutic effects. Discussion Most improvements persisted at follow-up, suggesting short-term neurotherapeutic effects. This case series demonstrates the promising utility of FES-assisted gait-training interventions, tailored to target individual gait deviations, in improving walking performance.

Functional electrical stimulation systems are used as neuroprosthetic devices in rehabilitative interventions such as gait training. Stimulator triggers, implemented to control stimulation delivery, range from open- to closed-loop controllers. Finite-state controllers trigger stimulators when specific conditions are met and utilize preset sequences of stimulation. Wearable sensors provide the necessary input to differentiate gait phases during walking and trigger stimulation. However, gait phase detection is associated with inherent system delays. In this study, five stimulator triggers designed to compensate for gait phase detection delays were tested to determine which trigger most accurately delivered stimulation at the desired times of the gait cycle. Motion capture data were collected on seven typically-developing children while walking on an instrumented treadmill. Participants wore one inertial measurement unit on each ankle and gyroscope data were streamed into the gait phase detection algorithm. Five triggers, based on gait phase detection, were used to simulate stimulation to five muscle groups, bilaterally. For each condition, stimulation signals were collected in the motion capture software via analog channels and compared to the desired timing determined by kinematic and kinetic data. Results illustrate that gait phase detection is a viable finite-state control, and appropriate system delay compensations, on average, reduce stimulation delivery delays by 6.7% of the gait cycle.

IMPORTANCE Promoting patient mobility during hospitalization is associated with improved outcomes and reduced risk of hospitalization-associated functional decline. Therefore, accurate measurement of mobility with high-information content data may be key to improved risk prediction models, identification of at-risk patients, and the development of interventions to improve outcomes. Remote monitoring enables measurement of multiple ambulation metrics incorporating both distance and speed. OBJECTIVE To evaluate novel ambulation metrics in predicting 30-day readmission rates, discharge location, and length of stay using a real-time location system to continuously monitor the voluntary ambulations of postoperative cardiac surgery patients. DESIGN, SETTING, AND PARTICIPANTS This prognostic cohort study of the mobility of 100 patients after cardiac surgery in a progressive care unit at Johns Hopkins Hospital was performed using a real-time location system. Enrollment occurred between August 29, 2016, and April 4, 2018. Data analysis was performed from June 2018 to December 2019. MAIN OUTCOMES AND MEASURES Outcome measures included 30-day readmission, discharge location, and length of stay. Digital records of all voluntary ambulations were created where each ambulation consisted of multiple segments defined by distance and speed. Ambulation profiles consisted of 19 parameters derived from the digital ambulation records. RESULTS A total of 100 patients (81 men [81%]; mean [SD] age, 63.1 [11.6] years) were evaluated. Distance and speed were recorded for more than 14 000 segments in 840 voluntary ambulations, corresponding to a total of 127.8 km (79.4 miles) using a real-time location system. Patient ambulation profiles were predictive of 30-day readmission (