Human Activity Recognition (HAR) is vital for healthcare, safety, smart homes, and transportation, enhancing well-being, security, and efficiency. It supports patient care, fitness tracking, workplace safety, and personalized automation while aiding sports performance, rehabilitation, and urban planning. This paper employs a pre-trained deep convolutional neural network, ResNet50V2 model, to classify daily activities of older people using heatmaps from accelerometer data collected from young and older adults. It examines daily physical activities in older adults ranging from fit to frail, comparing their performance with training data from the same age group and assessing models on individuals both with and without walking aids. The proposed ResNet-50V2 model achieved 98.2% classification accuracy on HAR70+ dataset and 96.8% on a batteryless wearable sensor dataset. This study comprehensively presents the proposed method, tools, and analysis.