# Deep Learning in Left and Right Footprint Image Detection Based on Plantar Pressure
## 1 Introduction  
Cerebral palsy (CP) is a leading cause of physical disability in children. Currently, the spastic type of CP is the most common occurrence: 50 to 60% of all cases of CP [1,2]. People with spastic CP suffer primarily from impairments such as increased tone, muscle weakness, diminished selectivity, and joint contractures. These impairments contribute to poor performance of functional activities in people with spastic CP [3]. Spastic CP also affects ambulation ability, movement, and posture, with accompanying activity restrictions that significantly burden people with CP, their families, and society [4]. The impairment in neuromuscular control of the lower limb affects balance and ambulation ability in people with spastic CP. Ankle-foot orthosis is the primary intervention for improving balance and correcting asymmetrical gait [5]. The use of an orthosis reduces the range of motion of hip internal rotation and improves postural stability during standing and asymmetrical weight distribution over the foot, which results in better balance in people with spastic CP [6]. After orthosis treatment management is provided to people with spastic CP, a footprint can be used to evaluate balance and locomotion [7]. Identifying the dominant leg from the footprint can help evaluate orthosis treatment in improved postural control in spastic CP patients [8]. Foot supination occurs in the dynamic evaluation when the dominant leg transitions from heel contact to middle stance throughout the gait cycle, which is related to the foot balance index in orthosis treatment [9]. In people with spastic CP, the dominant leg was assumed to be ipsilateral [10]. Hence, proprioception error was related to the non-dominant leg, which may induce complex footprints [11]. Therefore, an assessed CP complex footprint would be beneficial in managing orthosis treatment to provide essential information on the dominant or non-dominant leg [12]. Precision detection of the left and right feet from complex footprints can positively decrease energy expenditure in the dominant or non-dominant leg and monitor the association between the maximum step length test and the walking efficiency in CP patients [13]. However, CP footprints have limitations derived from complex footprint features, which would be incomprehensible in determining the left and right feet in clinics [14]. For example, scissor gait and toe walking are prominent among CP patients [15]. Scissor gait, usually walking with crossing legs due to spastic paraplegia or excessive contraction of hip adductor muscles [16], may lead to abnormal foot progression angle in footprint images [17]. Abnormal foot progression angle affects the recognition of left and right footprints [18]. The other example is toe walking, a bilateral gait abnormality in which a normal heel strike is absent, and weight-bearing occurs through the forefoot caused by limb-length discrepancy, spastic equinus, and Achilles tendon contracture [19]. Determining the left and right feet based on footprint images in toe walking conditions may be difficult if the full foot pressure is not recorded, especially in the absence of the heel region on the footprint distribution. Regarding scissor gait and toe walking foot problems, it would be challenging to determine the left and right feet under abnormal foot progression angle and complex footprints in people with spastic CP. Deep learning has been used in footprints with image features to auto-detect foot identification [20]. Object detection is a popular deep learning method that trains on images and directly optimizes performance when making predictions [21]. Object detection can obtain good accuracy based on image features in the footprint, even under the limitation of CP footprint features such as abnormal foot progression angle and complex footprints. Due to the models' use of hierarchical feature extraction, the multi-layered representations from pixel to high-level semantic features are learned by a sequential structure [22].