Physical therapy (PT) exercise is an evidence-based intervention for non-specific chronic low back pain, spinal deformities and poor posture. Home based PT programs are aimed at strengthening core muscle groups, improving mobility and flexibility, and promoting proper posture. However, assessing unsupervised home-based PT outcomes is a generally difficult problem due to lack of reliable methods to monitor execution correctness and compliance. We propose a monitoring method consisting of a wearable sensor array to monitor three geodesic distances between two points on the surface of the shoulders and one point on the lower back. The sensor array may be built into a custom garment or a light weight harness wirelessly linked to a pattern recognition algorithm implemented in a mobile app. We use a new type of triangular stretch sensor array design which can generate a unique signature for a correct spine therapy exercise when performed by a specific subject. We conducted a pilot test consisting of three experiments: (i) two exercise patterns simulated by a mechanical device, (ii) one PT case of a scoliosis therapy exercise including spinal flexion, extension, and rotation performed by one volunteer patient, and (iii) a set of three lower back flexibility exercises performed by six subjects. Overall, the results of correctness recognition show 70-100% sensitivity and 100% specificity. The pilot test provides key data for further development including clinical trials. The significance of the method includes simplicity of design and training method, ability to test with simulated signals, and potential to provide real time biofeedback.