Obstacle crossing during walking requires higher level of neuromuscular activation than level walking and is an important task for everyday life, especially for elderly people or people with balance impairments (Chou and Draganich, 1997; Overstall et al., 1977). One of the most serious causes which increase the risk of falls and injury, especially in elderly people, is an unsuccessful obstacle crossing (Galna et al., 2009). In addition to this, fatigue could be an aggravating factor in such circumstances (Barbieri et al., 2013; Parijat and Lockhart, 2008). However, the available literature in the field of obstacle crossing under fatigued state is very limited.
According to the current literature, fatigue affects the accuracy of movements during various tasks (Knicker et al., 2011), which might have implications in obstacle crossing. Inaccuracy in movement during obstacle crossing could result in tripping on the obstacle or slipping during foot strike, which increases the risk of falling and raises the probability for injury. Therefore, fatigue is an important element of everyday life that could influence the pattern of obstacle crossing. Nonetheless, this issue has not been investigated in healthy subjects yet.
Walking over obstacles has four objectives: 1. to maintain balance, 2. to prevent tripping during crossing, 3. to prevent slipping during landing, and 4. to progress the body forward. In order to cross an obstacle successfully, numerous muscles have to be activated in a certain sequence and intensity. Fatigue affects the coordination and the motor control accuracy (Sparto et al., 1997) and this could have consequences during obstacle crossing. For example, it is known that co-activation of agonist and antagonist muscles offers better joint stability (Kellis, 1998; Nagai et al., 2013) and could facilitate accuracy of the limb movement (Gribble et al., 2003). Many researchers have studied the complex relationship between fatigue and co-activation of antagonist muscles. It has been shown that fatigue has consequences on the accuracy of elbow flexion and extension movements and this could be explained by the reduced level of antagonist muscle coactivation (Missenard et al., 2008). Considering the above, it could be argued that during obstacle crossing such reduction in co-activation of the ankle joint muscles under fatigue conditions could impede accuracy and increase the risk of tripping. However, this issue remains to be verified.
Considering the above, the aim of this study was to investigate the effects of fatigue on the EMG characteristics during obstacle crossing of different heights. It is hypothesized that decrease in torque output of the plantar flexor muscles due to fatigue will induce changes in the kinematic and EMG response during obstacle crossing which could be linked to increased risk of tripping, especially at higher obstacle heights. This study will comprise the basis for future research in the field of obstacle crossing in order to understand how the neuromuscular system adapts under fatigue conditions. This could have applications to other populations which are more prone to fatigue and/or have increased risk of falling.
Twelve healthy men (age 18 to 30 years old) with no previous musculoskeletal injuries on the lower extremities or low back, participated voluntarily in this research (Table 1). Participants with estimated body fat above 30% were excluded, in order to avoid low signal to noise ratio in EMG signals (Lindstrom and Magnusson, 1977). All individuals were not systematically trained (less than 2 times a week physical activity) and gave their written consent before their participation, in accordance to the guidelines of the Local Ethics Committee.
Subjects were instructed not to consume any alcohol or caffeine and not to exert any strenuous activity 24 hours prior to the examination. After the participants' arrival to the laboratory the anthropometric characteristics were measured (postural height, limb length, skinfold thickness). Lower limb length was measured with scaled tape from the anterior superior iliac spine to the lateral malleolus. This measurement was used to calculate the obstacle height for each individual separately. Body fat, expressed as percentage of body mass, was estimated by measuring the skinfolds of the triceps brachialis, the abdominal, the suprailiac, and the thigh with an analog skinfold caliper (#1127A, Lafayette Instruments, Indiana, USA), as indicated by Jackson and Pollock (1985).
After the warm-up (low intensity running on a treadmill for 5 minutes and stretching for the lower limb muscle groups), the subject laid supine on the chair of the dynamometer (CYBEX Norm, HUMAC/NORM system, USA) in a comfortable position. The knee was fully extended and the foot was positioned at 90[degrees] relative to the shank. The foot was fixed with Velcro straps on the dynamometer, so that the projection of the ankle joint rotation axis crossed approximately the axis rotation of the dynamometer. The participants performed 2-3 submaximal plantar and dorsiflexions, before the Maximal Voluntary Contraction (MVC) test began. MVC test included 5 plantar flexions and 5 dorsiflexions with 3 minutes rest. The best trial in torque output was analyzed as baseline value.
After the MVC test, the participants were instructed to walk on their preferred speed along an 8-meter walkway with an obstacle placed on the midway, perpendicular to the direction of walking, and parallel to the ground. The obstacle consisted of a light-weight, plastic tube (1 cm 0) that was placed on height-adjustable metal plates. The height of the obstacle was set at 10%, and 20% of the lower limb length of each participant. A series of trials with no obstacle were captured as well. These 3 conditions were assessed in random order. The self-selected pace was preserved for each participant during the whole experiment using a digital metronome (SAMWOO IMT - 020, Korea). Each individual started walking with the verbal cue "go", starting from the same point marked on the ground. Five trials for each obstacle height with a brief rest period in between were captured as baseline measurements.
As soon as the baseline measurements were assessed, the participants performed the fatigue protocol (modified Bruce protocol). More particularly, it consisted of walking/running on a treadmill with the increments in speed and inclination every 3 minutes, as shown in Table 2. During the session, heart rate was monitored (Polar, Model A3, Polar Electro, Oy, Finland) and when exhausted, the participant defined on a scale from 1 to 20 the extent of fatigue that they experienced, according to the Borg scale. Immediately after the end of the fatigue protocol, two gait trials for each condition (no obstacle, 10%, 20% obstacle) were assessed randomly followed by an MVC isometric plantar flexion. These tests were repeated 5 minutes after the end of the fatigue protocol. At the end of the experiment, the participants performed stretching exercises for their lower limbs (5 minutes).
Feet position tracking
In order to track the position of the feet relative to the obstacle three-dimensional trajectories were captured with 6 cameras (type M3) of a VICON 612 motion analysis system (Oxford Metrics, Ltd., Oxford, Oxforshire, UK). The sampling frequency was set at 120 Hz. Infrared reflective markers were fixed with adhesive tape, bilaterally, on the lateral malleolus, on the heel and between the 2nd and 3rd metatarsal distally. Two additional markers defined the position of the obstacle.
Data were processed offline with custom Matlab scripts (Matlab 7.0, Mathworks Inc.). The distance between the marker placed on the lateral malleolus and the horizontal line defined by the obstacle markers was calculated through the whole trajectory of the swing phase during crossing, and its minimum value was used to quantify how...